Introduction. E Ravussin* 1 and J-F Gautier 1

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1 International Journal of Obesity (1999) 23, Suppl 1, 37±41 ß 1999 Stockton Press All rights reserved 0307±0565/99 $ E Ravussin* 1 and J-F Gautier 1 1 Clinical Diabetes and Nutrition Section, National Institute of Health, NIDDK, Phoenix, Arizona 85016, USA Human obesity is the result of both environmental and genetic factors. In this manuscript, we brie y review the metabolic factors predicting body weight gain in Pima Indians, a population prone to obesity. The metabolic predictors of weight gain are: 1) a low metabolic rate, 2) low levels of physical activity, 3) low rates of fat oxidation, 4) insulin sensitivity, 5) low sympathetic nervous system activity, and 6) low plasma leptin concentrations. In contrast, obesity is associated with high metabolic rate, high fat oxidation, low insulin sensitivity and high plasma leptin concentration. This observation emphasizes the need to conduct prospective studies to obtain a better understanding of the etiology of obesity. In addition, genetic studies will help to identify new pathways involved in the pathophysiology of obesity. Keywords: body weight; energy expenditure; physical activity; respiratory quantity; insulin sensitivity; sympathetic nervous system Introduction The prevalence of obesity and overweight has reached epidemic proportions in some industrialized countries. For example, between 1976 ± 1980 and 1988 ± 1994, the prevalence of overweight subjects (body mass index, BMI 25.0) increased from 46% to 54% in the United States. During the same period, the prevalence of obesity, (de ned as BMI 30.0) increased from 14.5% to 22.5%. 1 The economic burden of obesity has been estimated to average $99 billion in 1995, including $52 billion in direct medical cost. 2 The increasing prevalence of obesity during the 20th century and the inverse relationship between obesity and both socio-economic status and physical activity, 3±5 provide clear evidence of potent environmental in uences on obesity. Also, obesity is positively related to television viewing, 6 parity 4 and smoking cessation. 4 Lastly, migration studies show that populations moving from a traditional to a westernized environment experience increase their prevalence of obesity and type 2 diabetes. 7 However, among individuals of similar ethnic origin living in a particular environment, variability in body size and body composition is mostly caused by genetically determined responses to that environment. Although the pathogenesis of obesity is not completely understood, 3,8 excessive accumulation of fat is probably due, in most cases, to interactions between genetic factors and environmental conditions. Historically, obesity has been viewed as behavioral in origin, stemming primarily from disordered patterns of food *Correspondence: Dr Eric Ravussin, Lilly Corporate Center, Drop Code 0545, Indianapolis, IN 46285, USA. intake and physical activity. Consequently, therapeutic approaches were targeted at modifying existing eating and exercising habits. However, the resistance of obesity to such treatment raises the concern that its origin may, in fact, not be primarily behavioral. Familial transmission of human obesity is well known, but family members not only share genes, but also diet, cultural background and many other aspects of lifestyle. Consequently, other methods are needed to separate the familial traits into genetic and environmental components. The study of twins, 9 for example, is based on the simple principle that identical twins share all their genes and a common environment, whereas fraternal twins share a common environment, but only half of their genes. Another strategy is to compare body size and body composition of adopted children with those of their biological and adoptive parents. 10 The ideal strategy is to combine the two approaches and to study twins adopted together or apart. 11 From such studies, it has been proposed that a large part of the variance in body size and body composition can be attributed to genetic factors. 12 The classical treatment of obesity, based on increased physical activity and decreased calorie intake, has not been successful. Approximately two thirds of the people who lose weight will regain it within one year, and almost all of them within ve years. 13 The lack of ef ciency in these therapeutic approaches is probably due to our incomplete understanding of the precise etiology or etiologies of obesity. Therefore, it is necessary to identify metabolic risk factors, in order to better understand the pathogenesis of this condition and to lead to better treatment. In this manuscript, we brie y review the metabolic risk factors identi ed among Pima Indians, a population with one of the highest prevalences of obesity in the world. 14

2 38 metabolic rate Obesity is clearly associated with a high absolute metabolic rate, both in resting conditions or over 24 h. 15,16 Therefore, obesity cannot be attributed to a low absolute metabolic rate, as is often proposed. However, the scatter about the regression line between metabolic rate and body size suggests that, at any given body size, individuals can have a `high', `normal' or `low' relative metabolic rate. Is a person with a low relative metabolic rate, that is, low for body size and body composition, at greater risk of gaining weight than one with a normal or a high relative metabolic rate? This question has been examined in three different populations. Roberts et al 17 measured energy expenditure by doubly-labeled water (DLW) in 18 infants at 3 months of age and divided them post-hoc into overweight and normal weight groups at age 12 months. Energy expenditure (mostly activity) was on average 20% lower in those who became overweight at 12 months than in those who did not. Similarly, Grif th et al 18 reported that a low energy expenditure in girls aged 5 y correlated negatively with BMI at adolescence. From our own studies of adult non-diabetic Pima Indians we found that a low relative metabolic rate (resting and 24 h) adjusted for differences in fat-free mass (FFM), fat-mass (FM), age and gender was a risk factor for body weight gain. 19 However, the low relative metabolic rate accounted for only 40% of the weight gain and the metabolic rate became normal, if not high, at the new higher body weight. Consequently, the results indicate that a low metabolic rate may also be a marker for abnormal regulation of dietary intake. They also indicate a need to better understand the factors underlying the variability in metabolic rate among individuals of similar body size and body composition. Finally, they suggest that safe drugs designed at increasing metabolic rate may be useful in the treatment of obesity. physical activity Another component of 24 h energy expenditure is the energy cost of spontaneous physical activity, which accounts for 8 ± 15% of total daily expenditure. 15 Consistent with the fact that most cross-sectional studies show a decrease in spontaneous physical activity in obese subjects, our longitudinal studies showed that even in the con ned environment of a respiratory chamber, spontaneous physical activity is a familial trait and that a low level of spontaneous physical activity is associated with subsequent weight gain in males, but not in females. 20 Of course, prospective studies in which free-living physical activity is measured (DLW technique) are needed to objectively assess what most scientists believe, that is, that low physical activity is a major predisposing factor for weight gain in individuals and in populations. 5 rates of fat oxidation The composition of nutrient intake has been shown to be an important factor in the genesis of obesity, and consequently, one might expect that the composition of nutrient oxidation would also play a role. The nonprotein respiratory quotient (RQ), which is an index of the ratio of carbohydrate to fat oxidation, ranges from a value of about 0.80 after an overnight fast (when fat is the main oxidative substrate) 21 to values close to 1.00 after a large carbohydrate meal (when glucose is the major substrate) 22. Apart from the effect of diet composition, the RQ is also in uenced by recent energy balance (negative balance causing more fat oxidation), gender (females tend to have reduced fat oxidation), adiposity (higher FM leads to higher fat oxidation) and family membership, suggesting genetic determinants. In a recent study, Toubro et al 23 found that substrate oxidation rates, measured by the RQ, exhibit familial correlation, after adjustment for energy balance, gender and age. In a longitudinal study of Pima Indians, the 24 h RQ showed considerable inter-individual variability (part of it aggregating in families) and predicted weight gain. 24 Those in the 90th percentile for RQ (`low fat oxidizers') had a 2.5- fold greater risk of gaining 5 kg body weight than those in the 10th percentile (`high fat oxidizers'). This effect was independent of a relative low or high 24 h metabolic rate. Similar results were found in Caucasian volunteers participating in the Baltimore Longitudinal Study on Aging. 25 In support of these observations, recent studies have demonstrated that post-obese volunteers consistently have a high RQ, that is, a low rate of fat oxidation, 26,27 and those who are able to maintain weight loss have lower RQs compared to those experiencing weight relapse. 28 Insulin sensitivity Insulin sensitivity is measured as the total glucose disposal stimulated by insulin during a glucose clamp, and is the sum carbohydrate oxidation and non-oxidative glucose disposal (mainly storage). Since higher rates of carbohydrate oxidation (as measured by the 24 h RQ) predict weight gain, we investigated whether insulin sensitivity would also predict weight gain in Pima Indians. Subjects in the 90th percentile (most insulin sensitive) were 3 ± 4 times more likely to gain 10 kg body weight compared with those in the 10th percentile (most insulin resistant), and this effect was more closely related to the oxidative than the nonoxidative component. 29 Thus, insulin sensitivity predicts weight gain, or conversely, insulin resistance is associated with signi cantly lower rates of weight gain. Similar observations were reported in Mexican Americans 30 and Caucasians from the San Luis Valley Diabetes Study. 31 Yost et al 32 also reported that increased insulin sensitivity predicts weight regain following sustained weight loss. At least in adults, it appears that insulin resistance is a mechanism

3 counteracting further weight gain. 33 In contrast, children aged 5 ± 10 y, with a high fasting plasma insulin concentration (probably re ecting insulin resistance) are more likely to become overweight 7 ± 8 y later (especially boys). 34 Taken together, these results indicate that hyperinsulinemia promotes excess weight gain during growth in childhood (perhaps through the antilipolytic effect of insulin) and that subsequent insulin resistance then protects adults against further weight gain. sympathetic nervous activity Studies in Caucasians indicate that the activity of the sympathetic nervous system (SNS) is related to the three major components of energy expenditure, that is, resting metabolic rate (RMR), 35 the thermic effect of food 36 and spontaneous physical activity. 37 Further indications of the possible role for SNS activity in the regulation of energy balance in humans comes, from a recent study 38 in which low SNS activity was associated with a poor weight loss outcome in obese subjects treated with diet. Furthermore, Pima Indians (who are prone to obesity) have low rates of muscle sympathetic nerve activity compared to weightmatched Caucasians. 35 In a prospective study ( y follow-up), we found that the baseline urinary excretion rate of norepinephrine, a good index of SNS activity, was negatively correlated with body weight gain in male Pima Indians. 39 Thus, a low activity of the SNS is associated with the development of obesity. plasma concentration of leptin Leptin, the product of the OB gene, is a hormone produced by the adipose tissue that inhibits food intake and increases energy expenditure. 40 To investigate whether individuals prone to weight gain are hypoleptinemic, we measured fasting plasma leptin concentrations in two groups of weight-matched, nondiabetic Pima Indians that we followed for approximately 3 y, 19 of whom subsequently gained weight (average weight gain 23 kg) and 17 of whom maintained their weight within 0.5 kg. After adjustment for initial percent body fat, mean plasma leptin concentration was lower in those who gained weight than in those whose weight was stable. 41 Despite the leptin resistance apparent in obesity, 40 this data indicates that relatively low plasma leptin concentrations may play a role in the development of obesity in humans. The search for obesity=energy metabolism genes Since obesity and its major predictors are clearly genetically determined, many research groups are now trying to uncover one or more major genes contributing to obesity by genetic linkage studies. 42 We have recently completed an autosomal genomic scan, to search for such obesity=energy metabolism genes in Pima Indians. Obesity was assessed by percent body fat (hydrodensitiometry) and energy metabolism, measured in a respiratory chamber over 24 h. Linkage analysis was made with 516 microsatellite markers (median spacing of 6.4 cm) in 362 siblings who had measurements of body composition and 220 siblings who had measurements of energy metabolism. 43 Suggestions of linkage to percent body fat were found at 11q21-q22 and 18q-21. The best evidence for linkage to metabolic rate was also found on chromosome 11 (11q23-q24). Finally, two suggestive linkages to substrate oxidation (24 h RQ) were found on 1p31-p21 and 20q Possible candidate genes include the leptin receptor on chromosome 1 and the agouti signaling protein on chromosome 20. Many loci underlying some of the variability in human obesity, will be uncovered from such genetic studies and possibly genes will be identi ed in the near future. Such approaches will help our understanding of the etiology of obesity and also provide the basis to design drugs to target obesity via effects on energy expenditure and=or food intake. Conclusions Over the past 10 years, we have identi ed at least six metabolic risk factors for body weight gain in Pima Indians, that is, a low metabolic rate, a low spontaneous physical activity, a low rate of fat oxidation, insulin sensitivity, a low activity of the SNS, and relatively low plasma leptin concentrations. The results indicate that inter-individual variability in these metabolic predictors of weight gain are closely involved in energy balance regulation and in determining an individual's body weight. In view of the known familial nature of these parameters, it seems likely that they contribute to the inherited tendency to obesity. Searching for obesity susceptibility genes by genetic linkage or association studies is, therefore, warranted. Table 1 Metabolic factors related to obesity: cross-sectional vs longitudinal studies Cross-sectional (associated with obesity) Longitudinal (predicts weight gain) Relative metabolic Normal or High rate Energy cost of physical Normal activity Fat oxidation Normal or High Insulin sensitivity High Sympathetic nervous High system activity Relative plasma leptin concentration High 39

4 40 Most of the above factors have been shown to be associated with obesity in cross-sectional studies, but in a reverse manner. For example, obesity is associated with high metabolic rate, high fat oxidation, low insulin sensitivity and high plasma leptin concentrations (Table 1). This observation, therefore, emphasizes the need to conduct prospective (not crosssectional) studies to understand the etiology of obesity. Indeed, cross-sectional studies could lead to opposite conclusions, since the increased FM associated with obesity seems to be the price paid to reach a new equilibrium with high metabolic rate, higher energy cost of physical activity, higher fat oxidation, lower insulin sensitivity, higher SNS activity and higher plasma leptin concentration. The implication that metabolic factors can promote weight gain or weight loss, and that the consequent weight change in turn in uences those factors beyond what would be predicted (on the basis of cross-sectional data), is intriguing and warranted further investigation and longitudinal studies. Our data also indicate that the relative stability of energy stores and, therefore, body weight, is the result of a complex interaction between metabolic factors and environmental conditions. Indeed, since most of these metabolic predictors seems to be inherited, it is likely that body weight results from the interaction between these parameters and the environment. Given a set of genes and a particular environment, body weight will stabilize at a point at which an equilibrium is reached. This indicates that rather than talking about a `set point', body weight will `settle' at a level at which energy expenditure, fat oxidation and food intake will be in equilibrium. 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