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The Obesity Epidemic: Pathophysiology and Consequences of Obesity F. Xavier Pi-Sunyer

Abstract PI-SUNYER, F. XAVIER. The obesity epidemic: pathophysiology and consequences of obesity. Obes Res. 2002;10:97S–104S. Obesity has reached epidemic proportions in the United States: more than 20% of adults are clinically obese as defined by a body mass index of 30 kg/m2 or higher, and an additional 30% are overweight. Environmental, behavioral, and genetic factors have been shown to contribute to the development of obesity. Elevated body mass index, particularly caused by abdominal or upper-body obesity, has been associated with a number of diseases and metabolic abnormalities, many of which have high morbidity and mortality. These include hyperinsulinemia, insulin resistance, type 2 diabetes, hypertension, dyslipidemia, coronary heart disease, gallbladder disease, and certain malignancies. This underscores the importance of identifying people at risk for obesity and its related disease states. Key words: obesity, abdominal obesity, body mass index, insulin resistance, epidemiology

Introduction The obesity epidemic in the United States is an unintended consequence of the economic, social, and technological advances realized during the past several decades. The food supply is low in cost and abundant, and palatable foods with high caloric density are readily available in prepackaged forms and in fast-food restaurants. Labor-saving technologies have greatly reduced the amount of physical activity that used to be part of everyday life. Finally, the

Division of Endocrinology, Diabetes, and Nutrition, New York Obesity Research Center, Columbia University College of Physicians and Surgeons, St. Luke’s-Roosevelt Hospital Center, New York, New York. Address correspondence to F. Xavier Pi-Sunyer, MD, Division of Endocrinology, Diabetes and Nutrition, New York Obesity Research Center, Columbia University College of Physicians and Surgeons, St. Luke’s-Roosevelt Hospital Center, 111 Amsterdam Avenue, 10th Floor, New York, NY 10025. E-mail: [email protected] Copyright © 2002 NAASO

widespread availability of electronic devices in the home has promoted a sedentary lifestyle, particularly among children. The prevalence of obesity, which is defined as a body mass index (BMI) of 30 kg/m2 or higher, has been increasing dramatically. According to the Behavioral Risk Factor Surveillance System, a cross-sectional telephone survey of noninstitutionalized adults ages 18 years or older conducted by the Centers for Disease Control and Prevention, the prevalence of obesity between 1991 and 1998 increased in all 50 states in the United States, in both men and women, and across all age groups (1). Notably, only 4 states had obesity rates of 15% or higher in 1991, but by 1998, 37 states had exceeded that level. The National Health and Nutrition Examination Surveys (NHANES) show that the prevalence of obesity rose gradually from 1960 to 1980, but in the period from the second survey (NHANES II: 1976 to 1980) until the third (NHANES III: 1988 to 1994), it increased markedly, from 14.5% to 22.5% (2). The increase in obesity prevalence noted in the NHANES surveys was also evident in both men and women, across all age groups, and across race-ethnic groups (Figure 1). The highest prevalence of obesity was found among women of minority groups. The combined prevalence of overweight (BMI, 25 to 29.9 kg/m2) and obesity also increased dramatically, from 46.0% to 54.4% in the period from the second to third NHANES surveys (2). The rates of overweight and obesity were highest among black women and Mexican-American men and women. BMI values in the United States population do not exhibit a normal distribution; rather, there is an enormous burden of higher BMI values that reflect the large percentage of people who are overweight or obese. In NHANES III, the BMI distribution included 16.0% with values of 25 to 27 kg/m2, 18.6% with values of 27 to 30 kg/m2, 16.1% with values of 30 to 34.9 kg/m2 (class I obesity), 5.1% with values of 35 to 39.9 kg/m2 (class II obesity), and 2.9% with values in excess of 40 kg/m2 (class III obesity) (3). Importantly, BMI values are strongly correlated with total-body fat content, indicating that the degree of obesity can be calculated simply from height and weight measurements (4). OBESITY RESEARCH Vol. 10 Suppl. 2 December 2002

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Obesity Epidemic, Pi-Sunyer

Figure 1: Increasing prevalence of obesity during the past several decades. Panel A shows the age-adjusted prevalence according to gender and race, and panel B shows the prevalence according to age group. Data are from the National Health Examination Survey (NHES) and the three National Health and Nutrition Examination Surveys (NHANES) (2).

Pathophysiology of Obesity Genetics vs. Environment The relative contributions of genetics and environment to the etiology of obesity have been evaluated in many studies. Although it varies from study to study, ⬃30% to 40% of the variance in BMI can be attributed to genetics and 60% to 70% to environment. The interaction between genetics and environment is also important. In a given population, some people are genetically predisposed to develop obesity, but that genotype may be expressed only under certain adverse environmental conditions, such as high-fat diets and sedentary lifestyles (5) (Figure 2). In the United States, as well as in other Western countries, greater numbers of people are being exposed to these adverse environmental conditions, and consequently, the percentage of people expressing the obesity genotype has increased. The role of genetics is illustrated by a study of identical young-adult male twins who were overfed by 1000 kcal/d over a 100-day period (6). The weight gain among participants ranged from 4.3 to 13.3 kg, but the variance in response was significantly lower within pairs of twins than among pairs. The similarity in response within pairs was evident for body weight, percentage of fat, fat mass, and estimated subcutaneous fat. In other words, some twin pairs gained much more weight than other pairs. The concordance in response between identical twins demonstrates the impact of genetics on weight gain. A study of 540 adult adoptees 98S

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provides further evidence of the importance of genetics (7). The weight class of the adoptees in adulthood was classified as thin, median, overweight, or obese. Notably, the weight class was strongly related to the BMI of their biological parents, but it was unrelated to the BMI of their adoptive parents. In contrast, the weight of infants in their first 2 years was unrelated to maternal or paternal BMI (8). The impact of environment is illustrated by a study of Pima Indians (9); those residing in Arizona have among the highest prevalence of obesity. Despite the similar genetic predisposition, Pima Indians living a traditional lifestyle in

Figure 2: The interaction of genetics and environment (5).


a remote section of Mexico had significantly lower BMI than those living in the more affluent environment of Arizona (24.9 vs. 33.4 kg/m2; p ⬍ 0.001). These groups were separated ⬃700 to 1000 years ago and now differ in terms of both diet and energy expenditure. Pima Indians living in Mexico eat a diet with less animal fat and caloric density and more complex carbohydrates than those in Arizona, and they also have greater energy expenditure from physical labor. Metabolic Predictors of Weight Gain The development of obesity occurs when the caloric intake is disproportionate to the energy expended. Three metabolic factors have been reported to be predictive of weight gain: a low adjusted sedentary energy expenditure, a high respiratory quotient (RQ; carbohydrate-to-fat oxidation ratio), and a low level of spontaneous physical activity. The resting metabolic rate (RMR) is strongly correlated to fat-free mass (FFM) in both men and women (10). However, RMR is only one component of the total daily energy expenditure, which also includes the thermic effect of food and physical activity (11). Daily energy expenditure can be measured in a respiratory chamber or in free-living people by the doubly labeled water method. The contribution of low energy expenditure to the development of obesity was evaluated in several studies of Pima Indians. In a study of 95 subjects, the mean 24-hour adjusted energy expenditure was 36.3 kcal/kg FFM, but it varied considerably among the participants, from 28 to 42 kcal/kg FFM (12). Notably, this thermogenic response was correlated inversely with the change in body weight over a 2-year follow-up (r ⫽ ⫺0.39, p ⬍ 0.001). People with low adjusted energy expenditure were four times more likely to gain 7.5 kg during follow-up than those with high adjusted energy expenditure. Similar results were found in a second group of 126 subjects who were followed up for 4 years (12). Of those with low RMR, 28% had a 10-kg body weight gain during follow-up compared with ⬍5% of those with high RMR. Finally, in a group of 94 siblings from 36 families, the 24-hour energy expenditure tended to aggregate in families, such that some families showed low levels and others had high levels of energy expenditure (12). This finding suggests that energy expenditure may have a familial determinant. The Pima Indians provide the most convincing evidence relating low RMR to weight gain, but other studies have failed to show the same relationship. For example, RMR was evaluated in 24 overweight postmenopausal women before and after reduction to a normal-weight state (13). The RMR declined during energy restriction and weight loss, but it returned to baseline once energy balance was restored in the weight-reduced state. At this time, the RMRs among women who lost weight were not significantly different from the RMRs in a control group of never-overweight

women. After a mean follow-up of 4 years, the weightreduced women regained an average of 87% of their lost weight, but the weight gain was unrelated to their RMRs. Moreover, women in the control group had minimal weight gain over the same period, although their RMRs were comparable. The studies in Pima Indians measured 24-hour energy expenditure in a respiratory chamber. However, when total energy expenditure under free-living conditions was measured using the doubly labeled water method, total energy expenditure/RMR ratios ⬎1.75 were associated with lower BMI in men and less weight gain in previously obese women (14). RQ is the second potential metabolic predictor of weight gain. A low RQ of 0.7 suggests that a person is oxidizing more fat than carbohydrate, whereas a ratio of 1.0 suggests that more carbohydrate than fat is being oxidized (11). The relationship between RQ and weight gain was evaluated in nondiabetic Pima Indians who were fed a weight-maintenance diet (15). In a group of 152 subjects, the 24-hour RQ varied from 0.799 to 0.903. Notably, the 24-hour RQ correlated with changes in body weight during a mean follow-up of 25 months (r ⫽ 0.27, p ⬍ 0.01). Subjects in the 90th percentile of RQ were 2.5 times more likely to gain at least 5 kg than those in the 10th percentile, and this effect was independent of 24-hour energy expenditure. It is believed that a sedentary lifestyle also has an impact on weight gain, but it remains to be shown in a well-designed longitudinal study. According to the 1996 Surgeon General’s Report on Physical Activity and Health, participation in physical activity decreases with age. In each age group, more women than men do not participate in physical activity. For example, by the age of 45, ⬃18% of men and 30% of women are not regularly engaged in physical activity. Several other factors also are associated with overweight, but it is not clear why or how they have an impact. Sex, age, race, and socioeconomic status have an impact on weight gain, with overweight and obesity being more likely among women, older individuals, members of minority races, and those of low socioeconomic status. In the Behavioral Risk Factor Surveillance System, for example, the prevalence of obesity in 1998 was 18.1% among women and 17.7% among men (1). It increased from 12.1% among young adults ages 18 to 29 years and reached a maximum of 23.8% among those ages 50 to 59 years. African Americans had higher rates of obesity than Hispanics, who in turn had higher rates than whites. Finally, obesity prevalence declined with increasing levels of education, ranging from 24.1% among those who did not complete high school to 13.1% among those who graduated from college.

Patterns of Body-Fat Distribution and Risk for Certain Diseases Obesity increases risk for many disorders that are associated with high mortality and morbidity, including diabeOBESITY RESEARCH Vol. 10 Suppl. 2 December 2002

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Table 1. Disorders associated with obesity [adapted from Must et al. 1999; Pi-Sunyer 1993; USDHHS 2001 (16 –18)] ● Insulin resistance/hyperinsulinemia ● Type 2 diabetes ● Hypertension ● Dyslipidemia ● Coronary heart disease ● Gallbladder disease ● Cancer (prostate, endometrial, uterine, cervical, ovarian, colon, kidney, gallbladder, and postmenopausal breast) ● Premature death

● ● ● ● ● ● ● ● ● ●

Osteoarthritis Stroke Asthma Sleep apnea Breathing difficulties Complications of pregnancy Menstrual irregularities Hirsutism Increases surgical risk Psychological distress

tes, hypertension, coronary heart disease (CHD), dyslipidemia, gallbladder disease, and certain malignancies (16 –18) (Table 1). Not only does excess weight increase the risk of these disorders, but the pattern of fat distribution is important in many of these conditions. Men are more likely to have abdominal or upper-body obesity, whereas women are more likely to have a gluteofemoral or lower-body pattern of fat distribution. However, as women gain weight, they become more likely to develop abdominal and upper-body fat. There are now numerous prospective longitudinal studies showing that a pattern of upper-body fat distribution is independently associated with higher risk of developing diabetes and cardiovascular disease (19 –24). Moreover, cross-sectional studies show that abdominal fat distribution is related to various metabolic abnormalities and disorders in a manner similar to their relationship with high BMI or total fat burden (25–27). These metabolic abnormalities include atherogenic profile, high fibrinogen levels, hypertension, insulin resistance, hyperinsulinemia, glucose intolerance, arthritis, menstrual irregularities, and gallbladder disease.

Disorders Associated with Obesity Insulin Resistance and Hyperinsulinemia A reduction in sensitivity to insulin can occur through an inherited defect, or it can be acquired as a consequence of obesity. The impact of obesity is independent of genetic factors, as illustrated by a study of 23 sets of identical twins who were discordant for weight (28). Within both male and female twin pairs, the obese member had higher fasting insulin levels and showed lower insulin sensitivity in the 75-g oral glucose tolerance test than the non-obese member. 100S


These differences were particularly evident among members with high abdominal fat distribution. Once hyperinsulinemia and insulin resistance is acquired, it starts a cascade of metabolic changes that leads to diabetes, dyslipidemia, hypertension, hypercoagulability, and eventually cardiovascular disease. The relationship between insulin sensitivity and BMI is illustrated using the minimal model technique of Bergman (29). In a group of nondiabetic persons whose BMI ranged widely, insulin sensitivity was inversely correlated with BMI (30). The lower insulin sensitivity in obese subjects is also evident when insulin levels are measured over a 24-hour period (31). In both the fasting and postprandial states, obese subjects require insulin levels that are several times higher than non-obese subjects to maintain normal glucose tolerance. On a cellular level, insulin binds to its receptor on the surface of target cells, thereby causing tyrosine autophosphorylation and consequent intracellular signaling. These events culminate in cellular responses, such as the translocation of glucose transporters to the cell surface to allow glucose uptake for use or glycogen storage. In obesity, however, insulin signaling is defective. Insulinstimulated protein kinase activity of the insulin receptor, which mediates tyrosine autophosphorylation, is reduced in obese subjects relative to non-obese ones, and it is further reduced in obese type 2 diabetes patients (32). Furthermore, obesity is associated with other postreceptor binding defects in insulin action, including impaired generation of second messengers, diminished glucose transport, and abnormalities in some critical enzymatic steps involved in glucose use (33,34). Abnormalities in lipogenesis and protein synthesis also occur in obesity (35). However, obese subjects with depressed insulinmediated glucose transport can recover this response after weight loss (33). The increased levels of free fatty acids (FFAs) found in obese individuals also contribute to the defects in glucose use and storage. As body fat increases, the rate of lipolysis rises, leading to increased FFA mobilization and consequently to increased FFA oxidation in muscle and liver (Figure 3). In turn, glucose use by muscle declines as FFA is used as an alternate energy source, and hepatic glucose production increases in response to the higher FFA oxidation. These actions result in hyperglycemia and impaired glucose tolerance. This mechanism is particularly important among individuals with upper-body obesity. The plasma FFA turnover rate was higher among women with upperbody obesity compared with those with lower-body obesity or non-obese women (36). Moreover, the women with upper-body obesity showed lower glucose disposal and greater hepatic glucose production than the women with lowerbody obesity, who in turn had smaller defects than the


Figure 3: Effect of increased lipolysis on glucose use and gluconeogenesis.

non-obese controls (36,37). Thus, obesity—particularly abdominal or upper-body obesity—increases risk for glucose intolerance. Type 2 Diabetes The relative risk of developing type 2 diabetes increases steeply with increasing BMI. This relationship is best demonstrated by the Nurses’ Health Study, which prospectively followed up more than 114,000 registered nurses for 14 years (38). Relative to women with BMI ⬍22 kg/m2, ageadjusted risk of developing type 2 diabetes rose steadily with increasing BMI. Women with BMI of 35 kg/m2 or greater had a 93 times higher risk of developing diabetes than those with BMI of ⬍22 kg/m2. Even women who were overweight had higher risk; those with BMI of 25.0 to 26.9 kg/m2 and 27.0 to 28.9 kg/m2 had relative risks of 8.1 and 15.8, respectively. Weight gain is also important in determining risk of diabetes, particularly among those with higher baseline BMI. In a study of more than 51,000 male health professionals ages 40 to 75 years, subjects were grouped into tertiles according to their BMI at age 21 (39). Diabetes risk was positively correlated with absolute weight gain since age 21 in each tertile as well as with BMI at age 21. Subjects with a BMI of 24 kg/m2 or higher at age 21 who gained at least 11 kg had a 21 times higher risk of developing diabetes than those with a BMI of ⬍22 kg/m2 who gained ⬍5 kg since age 21. Abdominal obesity, as measured by waist-to-hip ratio, may be a stronger predictor of diabetes than BMI alone. In the Study of Men Born in 1913, a cohort of 54-year-old men were followed for 13.5 years (20); subjects were grouped

into tertiles of baseline BMI and waist-to-hip ratio. Subjects in the lowest tertile of waist-to-hip ratio did not have increased risk of developing diabetes, even if they were in the highest BMI tertile. However, in each of the other tertiles of waist-to-hip ratio, increasing BMI was associated with a greater probability of developing diabetes. Notably, the relative risk of developing diabetes was 30 times higher among those with the highest BMI and waist-to-hip ratio compared with those with the lowest waist-to-hip ratio. Hypertension The risk of hypertension also increases with increasing BMI. In the Nurses’ Health Study, risk of developing hypertension was determined among 41,541 predominantly white female nurses ages 38 to 63 years (40). During a 4-year follow-up, the risk of hypertension was increased among overweight and obese women relative to those with a BMI of ⬍23 kg/m2. The relative risk was 4.8 for those with BMI of 32 kg/m2 or higher. In addition to BMI, weight gain also dramatically increased risk of hypertension in each tertile of initial BMI, and weight loss reduced such risk (41). The NHANES III data also show that obesity increases risk of hypertension (42). Respondents with BMI of 30 kg/m2 or greater were twice as likely to have hypertension compared with non-obese subjects. Similarly, subjects with abdominal obesity, defined by a waist circumference of at least 102 cm for men and 88 cm for women, were twice as likely to have hypertension. The relationship among BMI, abdominal obesity, and the odds ratio for hypertension was evident among whites, African Americans, and Hispanics. OBESITY RESEARCH Vol. 10 Suppl. 2 December 2002

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The hypertension associated with obesity is characterized by an increase in vascular volume, whereas peripheral resistance is generally borderline or only slightly elevated (41). Several mechanisms may be involved in the development of hypertension in obese subjects: increased renal sodium and water absorption, sympathetic nervous system activation, changes in Na⫹/H⫹-ATPase activity, and growth factor–mediated structural changes to the vascular wall. In each case, hyperinsulinemia may be a contributing factor. Dyslipidemia Impaired glucose use and increased hepatic glucose output are not the only consequences of the higher FFA levels in obesity. Increased FFAs also affect lipid metabolism by increasing very-low-density lipoprotein production by the liver, reducing high density lipoprotein (HDL)-cholesterol levels, and increasing the number of small, dense lowdensity lipoprotein (LDL) particles (43). These smaller particles are better able to penetrate the arterial wall, more readily undergo oxidation and glycation, and are more atherogenic than larger, buoyant LDL particles. Even when the LDL cholesterol level does not change appreciably, atherogenic risk may be higher because of the presence of the smaller LDL particles. Taken together, these changes in lipoprotein profile are associated with increased risk of CHD. The impact of obesity on lipid metabolism is illustrated by a study of women with low or high abdominal obesity (44). The abdominal fat area in these two groups was 107 and 187 cm2, respectively. A control group of non-obese women had an abdominal fat area of 50 cm2. The women with high abdominal obesity had higher triglycerides and lower HDL-cholesterol levels than those with low abdominal obesity, which were in turn abnormal relative to the non-obese group. LDL-cholesterol was increased modestly in the obese women, but the LDL particles were more dense and atherogenic. CHD Fasting insulin levels are related directly to CHD mortality. In the Paris Prospective Study, for example, which evaluated CHD risk factors in more than 7000 working men, fasting insulin levels were an independent predictor of CHD death (45). Among those in the highest quintile of fasting insulin (⬎19 ␮U/mL), the incidence of CHD mortality was 2.5 times higher than among men with insulin of 5 ␮U/mL or less. Similarly, the fasting insulin level was found to be an independent risk factor for developing CHD in a casecontrol study of Canadian men (46). Even after adjusting for plasma triglycerides, apolipoprotein B, LDL-cholesterol, and HDL-cholesterol, the insulin level remained independently associated with CHD risk. A relationship between obesity and CHD mortality was demonstrated in the Nurses’ Health Study among women who had never smoked (47). The relative risk of CHD death 102S


increased significantly with increasing BMI (p ⬍ 0.001). Women with BMI of 29.0 to 31.9 kg/m2 and 32.0 kg/m2 or higher were at 4.6 and 5.8 times greater risk, respectively, than those with BMI values under 22.0 kg/m2. Moreover, the waist-to-hip ratio was strongly predictive of CHD mortality. Women in the highest quintile of waist-to-hip ratio had a relative risk of CHD death of 8.7 compared with those in the lowest quintile. Gallbladder Disease An independent relationship between obesity and gallbladder disease was shown recently in the Atherosclerosis Risk in Communities Study, which involved more than 12,700 subjects ages 45 to 64 years (48). In women, the risk of hospitalization for gallbladder disease increased with increasing BMI as well as higher waist-to-hip ratio. Overweight women had a 45% greater risk of hospitalization for gallbladder disease than those with BMI ⬍ 25 kg/m2. Among obese women, the risk increased further in relation to BMI; those who were morbidly obese had a relative risk of 2.5. Women with waist-to-hip ratios in the upper two quartiles had a 2-fold higher risk than those in the lowest quartile. In men, the relationship between BMI and risk of hospitalization for gallbladder disease was only seen among the morbidly obese group. The waist-to-hip ratio did not influence risk among men. Cancer The impact of obesity on cancer mortality was evaluated prospectively in a study of 750,000 men and women who were followed for 12 years (49). Men and women who were at least 40% overweight were 33% and 55% more likely, respectively, to die from cancer than those of average weight. Specifically, the mortality ratios for colorectal and prostate cancer in men and endometrial, uterine, cervical, ovarian, gallbladder, and breast cancer in women were highest among those who were at least 40% overweight. The highest mortality ratios were found for endometrial and uterine cancer. Similarly, in the Nurses’ Health Study, cancer mortality increased with increasing BMI (47). The cancer death rate for women with BMI of at least 32 kg/m2 was twice that for women with BMI of less than 19 kg/m2. The higher rate was predominantly because of increased mortality caused by colon, breast, and endometrial cancers. All-Cause Mortality In a study of 750,000 men and women, mortality due to any cause increased with higher body weight (50). In the Nurses’ Health Study, all-cause mortality showed a J-shaped relationship between BMI and overall mortality (47). The lowest mortality was found among women with BMI of 19.0 to 26.9 kg/m2, but then mortality increased steadily as BMI levels rose above 27 kg/m2.


Summary Several epidemiological surveys show that the prevalence of overweight and obesity has been increasing over the past two decades. The causes are multifactorial, but environmental, behavioral, and genetic factors have all been shown to contribute. Several risk factors for obesity are known, including a sedentary lifestyle, increasing age, and low socioeconomic status. These can help physicians identify which patients are obese or at risk of developing obesity. Elevated BMI and abdominal obesity are associated with a number of diseases and metabolic abnormalities that have high morbidity and mortality. This fact alone underscores the importance of the battle against the obesity epidemic.

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