Genetic Influences on Human Energy Expenditure ... - Michael Goran

Behavior Genetics, Vol. 27. No. 4. 1997

Genetic Influences on Human Energy Expenditure and Substrate Utilization Michael I. Goran1

Understanding the genetic factors of obesity requires consideration of the genetic basis of the underlying etiological factors including energy expenditure and substrate utilization. Genetic susceptibility studies suggest that altered energy expenditure and/or preferential substrate utilization are likely to be involved in the etiology of obesity. Twin and family studies suggest that there is a strongly heritable component to resting energy expenditure, substrate utilization, and the thermic response to feeding. Physical activity energy expenditure has been less well studied; new data are presented in young sib pairs that suggest moderate heritability of nonresting energy expenditure. Only a few candidate gene studies have been performed to examine the role of basic proteins involved in energy expenditure (the sodium—potassium ATPase and the uncoupling protein) or substrate utilization (fatty acid binding protein). The lack of information in this area warrants further investigation into genetic aspects of energy and substrate metabolism. KEY WORDS: Obesity; energy expenditure; metabolic rate; physical activity; fat oxidation; respiratory quotient.

potential energy stored within ingested macronutrients is released through oxidation/combustion in a gradual process, and the energy released from this process drives the production of ATP molecules. When cells require energy, ATP is hydrolyzed and the energy released is transferred into other energyrequiring processes. The cellular ATP pool is therefore in constant flux, and this continuous cycle of synthesis and hydrolysis provides a constant and controlled release of energy. Energy expenditure is typically measured in humans by either direct or indirect calorimetry. Direct calorimetry involves the measurement of heat production directly. This approach is technically demanding, especially in human studies, and is now infrequently used. Indirect calorimetry measures energy production via respiratory gas analysis. This approach is based on oxygen consumption and carbon dioxide production that occurs during the combustion (or oxidation) of protein, carbohydrate, fat, and alcohol. Respiratory gas analysis can easily

OVERVIEW OF THE COMPONENTS AND DETERMINANTS OF ENERGY EXPENDITURE AND SUBSTRATE UTILIZATION The energy consumed in food is required by the body for cellular and mechanical work. The liberation and transfer of energy from ingested macronutrients (protein, fat, carbohydrate, and alcohol) occur through a series of controlled and discrete biochemical pathways in which the potential energy from food is channeled through ATP. The potential energy stored within each molecule of ATP is then used for all energy-requiring activities. This process ensures that the body is provided with a gradual and constant energy store, rather than relying on a sudden release of energy from an immediate combustion of ingested food. Thus, the 1

Division of Physiology and Metabolism, Department of Nutrition Sciences, University of Alabama, Birmingham, Alabama 35294. Fax: 205-934-7049, E-mail: [email protected].

389 0001-8244/97/0700-0389S12 50/0 @ 1997 Plenum Publishing Corporation

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be achieved in humans either over short measurement periods at rest or during exercise using a face mask, mouthpiece, or canopy system for gas collection and over longer periods of 24 h (and longer) by having subjects live in a metabolic chamber. Indirect calorimetry has the added advantage in that the ratio of carbon dioxide production to oxygen consumption (the respiratory quotient; RQ), is indicative of the type of substrate (i.e., fat versus carbohydrate) being oxidized; for example, carbohydrate oxidation has an RQ of 1.0, and fat oxidation has an RQ close to 0.7. Finally, carbon dioxide production rates can be measured directly and used to estimate energy expenditure over extended "free-living" periods of 7-14 days using stable isotope methodology and doubly labeled water (Schoeller and Fjeld, 1991; Goran et al., 1995c). There are three main components of energy utilization. The largest component is resting metabolic rate, which is the energy expended to maintain the basic physiological function of the body (e.g., heartbeat, muscle function, respiration). Resting metabolic rate occurs in a continual process throughout the 24 h of a day. Resting metabolic rate is typically measured by indirect calorimetry under fasted conditions while subjects lie quietly at rest in the early morning for 30-40 min. In addition to resting metabolic rate, there is an increase in metabolic rate in response to food intake. This increase in metabolic rate is often referred to as the thermic effect of a meal (or meal induced thermogenesis) and is the energy that is expended in order to digest, metabolize, and store ingested macronutrients. The thermic effect of a meal is typically measured by continuous indirect calorimetry for 3-4 h following consumption of a test meal of known caloric content. The third component of energy expenditure is the increase in metabolic rate that occurs during body movement (includes exercise as well as all forms of physical activity). This metabolic rate is primarily the energy expended for the muscular contractions. The energy expended in physical activity can be measured under laboratory conditions also using indirect calorimetry during standard activities. Free-living physical activity-related energy expenditure over extended time periods of up to 2 weeks can be measured by the combination of doubly labeled water to measure total energy expenditure and indirect calorimetry to measure resting energy expenditure and the thermic effect of a meal.

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Each of the three components of energy expenditure is determined by various factors. Resting metabolic rate is determined primarily by the quantity of fat free mass, which alone explains 60-80% of the variance in resting metabolic rate (Ravussin and Bogardus, 1989; Garby et al., 1986). Since fatfree mass is a heterogeneous mixture of all nonfat body components, the metabolic rate associated with each kilogram of fat free mass is dependent on the quality of the fat-free mass, in terms of hydration and contribution of the different organs that make up the fat free mass. For example, skeletal muscle, compromises —43% of the total mass in an adult and contributes 22-36% of the resting metabolic rate, whereas the brain, which constitutes —2% of the mass, contributes 20-24% of the resting metabolic rate. In addition, the metabolic cost of each kilogram of fat-free mass decreases with developmental progression, probably due to developmental increases in the muscle mass:organ mass ratio within the fat-free mass (Weinsier et al., 1992). Thus, the relationship between energy expenditure and fat-free mass is not linear across all ages. The regression coefficient between resting metabolic rate and fat-free mass is estimated to be 79.2 kcal/kg fat-free mass between age 0 and age 2.5 years, 36 kcal/kg in 4-7-year-old children (Goran et al., 1994), 28.3 kcal/kg during adolescence, and 21.0 kcal/kg in adulthood (Weinsier et al., 1992). Resting metabolic rate is also influenced by fat mass even though fat mass is generally thought to be metabolically inert. Most studies report that fat mass contributes of the order of 10-13 kcal/kg to resting metabolic rate (Weinsier et al., 1992; Goran et al., 1994). In healthy adults, resting metabolic rate declines with age, over and above that expected given the age-related decline in fat-free mass (Poehlman et al., 1993a). Resting metabolic rate is also influenced by sex; males have a higher value than females by —50 kcal/day, and this effect is independent of fat-free mass (Arciero et al., 1993) and is consistent across the life span (Goran et al., 1994), More active subjects tend to have a higher resting metabolic rate, which may be explained, in part, by the residual effects of chronic exercise on metabolism (Poehlman et al., 1991, 1992), but other factors are involved since the higher resting metabolic rate persists long after the last bout of exercise has been completed. Collectively, fat-free mass, fat mass, age, sex, and phys-

Human Energy Expenditure and Substrate Utilization ical activity explain 80-90% of the variance in resting metabolic rate (Ravussin and Bogardus, 1989; Ravussin et al, 1986), and these factors must be considered when examining the independent genetic influence on energy expenditure. Note that the major determinants of resting metabolic rate are in themselves genetically influenced (fat-free mass, fat mass). Nevertheless, a portion of the unique variance in resting metabolic rate has been ascribed to genetic factors, and these studies are reviewed in more detail in the following sections. The thermic effect of meal ingestion is influenced primarily by the quantity and macronutrient quality of the ingested calories. The increase in metabolic rate that occurs after meal ingestion occurs over an extended period of at least 5 h; the cumulative energy cost is equivalent to —10% of the energy ingested. The metabolic rate of physical activity is determined by the amount of activity (i.e., time), the type of physical activity (e.g., walking, running, typing), and the intensity at which the particular activity is performed. The cumulative total daily energy cost of physical activity is highly variable. Even within the sedentary confines of a metabolic chamber, this component of energy expenditure is highly variable due to fluctuations and differences in spontaneous physical movements (Ravussin and Bogardus, 1989; Ravussin et al, 1986). In freeliving humans, physical activity represents the most variable component of total energy expenditure (Goran, 1995; Carpenter et al., 1995). EXAMINATION OF GENETIC INFLUENCES ON ENERGY EXPENDITURE BY STUDYING SUBJECTS WITH INCREASED SUSCEPTIBILITY TO OBESITY Preobese Children Obesity arises from a mismatch between energy intake and energy expenditure, such that intake exceeds expenditure. The mechanism of this dysregulation is unknown, and it is not clear whether obesity develops because of an excess in energy intake relative to expenditure, a reduced energy expenditure relative to intake, or a combination of both. In adults, studies on the role of energy expenditure in the development of obesity have yielded inconsistent findings (Seidell et al., 1992;

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Weinsier et al., 1993; Roberts et al., 1992; Rising et al., 1994). Using the doubly labeled water technique, several cross-sectional studies in adults (Prentice et al., 1986; Lichtman et al., 1992; Welle et al., 1992), adolescents (Bandini et al., 1990), and children (DeLany et al., 1996; Goran et al., 1995a; Treuth et al., unpublished), as well as a meta-analysis (Carpenter et al., 1995), suggest that absolute total energy expenditure is higher in obese individuals but is similar between lean and obese after controlling for differences in body composition. Several studies on the offspring of obese parents have been performed as these children may represent a model of the "preobese" state. Such studies are based on the fact that children of obese parents are highly likely to develop obesity (Garn and Clark, 1976). Thus, examination of the children prior to the development of obesity provides an opportunity to observe the natural history of the etiology of obesity. Using heart rate monitoring (to estimate total energy expenditure) and indirect calorimetry (to measure resting metabolic rate), energy expenditure was —20% lower in 8 children who had one or more parent with a history of obesity, compared to 12 children who had two parents with no history of obesity (total energy expenditure was 1174 ± 297 vs. 1508 ± 352 kcal/day; p < .01; mean ± SD), even though the two groups of children were matched for weight and lean body mass (Griffiths and Payne, 1976). However, this study should be interpreted with caution because of the small sample size and because the heart rate method does not provide reliable estimates of the total energy expenditure in children (Livingstone et al., 1990). Also, the hypothesis that reduced energy expenditure is a risk factor for further weight gain was never demonstrated. In a later study reporting 12-year prospective data, energy expenditure failed to predict the development of obesity (Griffiths et al., 1987). In infants bom to either underweight (prepregnancy weight below the 10th percentile) or overweight (prepregnancy weight above the 90th percentile) mothers (Roberts et al., 1988), total energy expenditure at 3 months of age was 20% lower in six of the infants who became overweight after 1 year, compared to the remaining infants (61.2 ± 6.5 vs. 77.2 ±3.4 kcal/kg/day; p < .05). Interestingly, this finding was refuted in a much larger study (n = 124 infants) by other investigators from the same Institute (Davies et al., 1995)

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in which there were no significant correlations between total energy expenditure or sleeping metabolic rate in infants and maternal or paternal body mass index (weight divided by height squared). In the interpretation of the original study (Roberts et al., 1988), it is important to consider that the "control" infants were bom to severely undernourished mothers whose prepregnancy weights were less than the 10th percentile. Although this experimental design is useful for creating a paradigm that will maximize the chances of seeing an effect, an alternative explanation of the findings could be that energy expenditure is higher in babies born to undernourished mothers. Furthermore, there were only six infants with overweight mothers who gained excess weight in the first 3 months of life. This low sample size is a major concern given that two of the infants were outliers with an energy expenditure below the expected physiological range. The effects of parental obesity on energy expenditure in offspring were examined in a crosssectional study (Goran et al., 1995a) of 73 children (5 ± 0.9 years; 20.4 ± 3.8 kg). There were no significant correlations between any component of energy expenditure in children (total, activity, and postprandial resting) and body fat in children or body fat in mothers or fathers. In analysis of covariance (using fat-free mass as a covariate), there were no significant effects of sex in children, obesity in mothers, or obesity in fathers on total or physical activity-related energy expenditure in children (Goran et al., 1995a). However, there was a significant effect of sex and a significant interaction between obesity in mothers and obesity in fathers on resting energy expenditure in children (Goran et al., 1995a). Resting energy expenditure was ~6% lower in children when either mothers or fathers only were obese, relative to children with either two nonobese or two obese parents (Goran et al., 1995a). Total energy expenditure and its components have been examined in other groups of children at high risk of developing obesity. In Mohawk Indian children in upstate New York, the prevalence of obesity is 44% (Jackson, 1993). In a subgroup of these children who were matched for fat and fatfree mass to a group of Caucasian children living in Burlington, Vermont, total energy expenditure by doubly labeled water, adjusted for fat-free mass, was significantly higher in the Mohawk Indian chil-

Goran dren by 100-150 kcal/day (Goran et al., 1995b). In addition, resting energy expenditure has been shown to be normal in Pima Indian children and in younger Mohawk children (4—7 years old) relative to Caucasian children (Fontvieille et al., 1992; Goran et al., 1995b). In Birmingham, Alabama, the prevalence of obesity (ideal body weight greater than 120%) is higher in African-American (26% in boys and 38% in girls aged 10 years) compared to Caucasian (21% in 10-year-old boys and girls) children (Figueroa-Colon et al., 1994). In 35 Caucasian children and 64 African-American children (Goran et al., unpublished data), there was no significant difference in total energy expenditure between sexes or ethnic groups, after controlling for soft lean tissue mass by DXA (adjusted mean values, 1688 ±41 kcal/day in Caucasian versus 1639 ± 31 kcal/day in African-American children). Thus, reduced energy expenditure does not necessarily explain the greater prevalence of obesity in subgroups of the pediatric population at greater risk of obesity. Interestingly, "genetic" forms of obesity in humans are associated with a lower total energy expenditure. In Prader-Willi syndrome, the total energy expenditure was 16% lower than in controls (Schoeller et al., 1988). In Down syndrome, the resting metabolic rate was 20% lower than control subjects, independent of differences in the body composition (Allison et al., 1995). In Pima Indians, a significant inverse relationship has been observed between the ratio of total to resting energy expenditure and percentage body fat (Rising et al., 1994). However, as discussed previously (Goran, 1995), these data should be interpreted with caution due to the complexities of data normalization.

Studies of Obesity and Postobesity Models in Adults Two types of studies have been used in adults to examine the role of energy expenditure in the development of obesity. These approaches are longitudinal studies of weight gain and intervention studies examining differences in energy expenditure in obese subjects after weight loss. The postobese model is based on a within-individual examination of phenotypic change after body weight has returned to "normal." Thus, if after

Human Energy Expenditure and Substrate Utilization

weight loss, the resting metabolic rate remains low, the implication is that a lower resting metabolic rate is an inherent factor in the etiology of obesity. However, carefully controlled metabolic studies are required to account for the likelihood that resting metabolic rate may also change due to factors such as reduced intake. Nevertheless, the examination of formerly obese subjects has been used widely as a model of genetic susceptibility. Seidell et al. (1992) failed to demonstrate any effect of resting metabolic rate in predicting 10year weight gain in 775 men. In a more controlled metabolic study, Weinsier et al. (1995) showed that neither the resting metabolic rate nor the thermic effect of a test meal predicted 4-year weight gain in 48 previously obese women who returned to their obese state over the 4-year period. In Pima Indians, a relatively low 24-h energy expenditure (and not resting metabolic rate) did predict the 2to 4-year-weight gain (r = — .39), and in postobese women, 24-h energy expenditure in a metabolic chamber is lower than in weight-matched controls (Ravussin et al., 1988). These data suggest that if energy expenditure does play a role in the development of obesity, the effect is likely to reside in the nonresting, physical activity related component. However, because of the difficulty in measuring physical activity-related energy expenditure, relatively few studies have yet to address this issue. Leibel et al. (1995) showed that physical activityrelated energy expenditure (estimated indirectly from caloric titration) was lower after weight loss, suggesting a role in the etiology of obesity. Larson et at. (1995) examined 11 postobese men and women and found that 24-h energy expenditure in a chamber was similar to that of a control group matched for age and body composition. However, the postobese individuals had lower rates of fat oxidation, suggesting that a reduction in the ability to oxidize dietary fat may increase susceptibility to gain weight. Thus, when Astrup et al. (1994) fed diets differing in macronutrient content, postobese women were more likely than weight-matched controls to accrue a positive fat balance, due to a failure in adjusting fat oxidation to match its level of consumption, as well as to a reduction in 24-h energy expenditure. Collectively, these studies point to a potential role for physical activity-related energy expenditure and the ability to oxidize fat calories in the etiology of obesity.

393 GENETIC STUDIES EXAMINING HUMAN ENERGY EXPENDITURE In the previous section we provided the background for explaining the role of energy expenditure in the development of obesity and the use of genetic susceptibility models to examine the potential genetic link in this process. In this section we review the limited number of available studies that have examined the genetic link more closely, through heritability estimates from twin and family studies, as well as potential genetic linkage through candidate gene analysis. Unmeasured Genotype Studies that Provide Heritability Estimates for Energy Expenditure Studies on Resting and 24-h Metabolic Rate Several studies have estimated the heritability of resting metabolic rate based on assessment of intrafamilial correlation coefficients. In a study examining familial resemblance among parent-child and monozygotic (MZ) and dizygotic (DZ) twins, the heritability estimate for resting metabolic rate measured by indirect calorimetry, independent of age, sex, and fat-free mass was of the order of 25-40% for parent-child and ~70% based on familiality among twin pairs (Bouchard et al., 1989). Henry et al. (1990) examined resting metabolic rate by indirect calorimetry in 14 pairs of MZ twins and 12 pairs of DZ twins aged 18-35 years. The withinpair correlation for resting metabolic rate was .80-. 85 for the MZ twins and was not significant for the DZ twins. Fontain et al. (1985) examined resting metabolic rate in 20 MZ twins and 19 DZ adult twins. Resting metabolic rate was measured by indirect calorimetry and body composition by underwater weight. The twin-twin correlation for resting metabolic rate was .45-.81 for MZ twins and .21-.44 for DZ twins, depending on how the data were expressed. Ravussin et al. examined familial resemblance of resting metabolic rate among 130 subjects from 54 Pima Indian families (Ravussin and Bogardus, 1989; Ravussin et al., 1986). After adjusting for fat-free mass, age, and sex, there was significant clustering of resting metabolic rate within families. The familial effect was estimated to explain 41% of the remaining variance in resting metabolic rate. For a similar analysis for 24-h metabolic rate in a chamber, the familial effect was

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estimated to explain 28% of the unique variance (Ravussin and Bogardus, 1989; Ravussin et al., 1986). Similarly, Zurlo et al. (1990) have reported that the familial resemblance in adjusted 24-h substrate utilization in weight-stable Pimas fed a fixed diet is 28%. Heritability Estimates for TEM The heritability. estimate for the energy expended in response to a fixed ingestion of a carbohydrate meal was 40-50%, based on the similarity of response between parent-child and twin pairs (Bouchard et al., 1989). Changes in resting metabolic rate and the thermic effect of a test meal were examined in six male MZ twin pairs in response to 22 days of overfeeding (Poehlman et al., 1986). Resting metabolic rate was unchanged and there was no relationship for the within-pair response to overfeeding. There was, however, a significant genetic dependence of the magnitude of change in metabolic rate for the 2.5 h following a 1000-kcal challenge. The range in change in the thermic effect of the meal in response to the overfeeding was from a 4% increase to a 21% increase, and the intrapair resemblance was 0.7, suggesting similarity within twin pairs for the response. Heritability Estimates for Exercise-Related Energy Expenditure Studies that have examined the genetic basis of exercise-related energy expenditure are even more limited than those for resting metabolic rate and the thermic effect of a meal. Studies of exercise-related energy expenditure are complicated by the difficulty in measuring this component of energy expenditure under free-living or habitual living conditions. The metabolic rate during submaximal exercise under laboratory conditions was similar in parent-child and twin pairs, suggesting a heritability estimate of 46%, but this effect disappeared at work intensities greater than six times the resting metabolic rate (Bouchard et al., 1989). Using a questionnaire technique, physical activity was estimated in 1537 MZ and 3057 DZ twins from the Finnish twin registry and suggested a heritability estimate of 62% (Kaprio et al., 1981). Familial correlations of physical activity (estimated by questionnaire) were reported to be lower, in the

Goran range of .12-.28, among 18,073 subjects from the Canadian Fitness Survey (Perusse et al., 1988). Using activity diaries from 1610 subjects from the Quebec Family Study, Perusse et al. (1986) found that the transmissible variance for physical activity was 29%. Using caltrac accelerometer to measure movement, Moore et al (1991) showed that active parents were 2-3.5 times as likely to have active children and that children of two active parents were almost 6 times as likely to be active, compared to children of two inactive parents. Thus, there is some evidence to suggest that energy expenditure during standard submaximal exercise is genetically influenced and that physical activity by questionnaire has a heritability estimate of 2962%. There are no published twin and family studies that have used doubly labeled water to examine total "free-living" energy expenditure. Thus, we present some new data for this review on sib-pair correlations for energy expenditure among 37 young (5 to 9 years of age) sib pairs (the group included 5 dizygotic twin pairs and no monozygotic twin pairs). The group included 16 AfricanAmerican sib pairs, 19 Caucasian sib pairs, and 2 Mohawk Indian sib pairs (because of the low sample size, we did not analyze the sample by ethnic group). Resting energy expenditure was measured using indirect calorimetry under fasting (for the African-American pairs) or postprandial (for the Caucasian and Mohawk Indian) conditions, total energy expenditure was measured over 14 days by doubly labeled water, and fat and fat-free mass were measured by dual-energy X-ray absorptiometry (DXA) in the African-American children) or estimated (in the other groups) from an anthropometric prediction equation that we have previously validated against DXA in children (Goran et al., 1996). The physical characteristics, body composition, and energy expenditure data of these children are provided in Table I. In these data, the significant predictors of RQ were fat and fat-free mass; after adjusting for these variables, there was a significant correlation for RQ within sibs (r = .5, p = .002; Fig. I). Similarly, for resting metabolic rate, fat-free mass was the only significant determinant, and after adjusting for this variable a significant correlation within sibs was noted (r = .41, p = .01; Fig. 1). For total energy expenditure, the significant determinants were fat-free mass and


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Table I, Physical Characteristics, Body Composition, and Energy Expenditure in 37 Young Sib Pairs Sib 1

Age (years) Height (cm) Weight (kg) Fat mass (kg) Fat-free mass (kg) Resting metabolic rate (kcal/day) Total energy expenditure (kcal/day)

5.5 114 22.0 4.7 17.0

Sib 2 1.2 11 5.7 3.3 3.0

7.1 124 27.3 6.5 19.8

± ± ± ± ±

1.5 11 7.5 4.5 3-2

1074 ± 146

1149 ± 141

1436 ± 322

1574 ± 337

resting metabolic rate. After adjusting for fat-free mass there was no significant correlation between sib pairs; however, when adjusting for resting metabolic rate, a significant correlation between sib pairs was noted (r = .33, p = .05). The familial correlations for RQ (.25) and resting energy expenditure (.17) agree with previous studies (see above). The new finding is the sib-pair correlation for nonresting energy expenditure of .11. Since this value was derived after adjusting data for resting metabolic rate, it seems likely that there are separate genetic influences on resting metabolic rate and nonresting energy expenditure. CANDIDATE GENE ANALYSIS

More recently, several studies have begun to examine whether there are specific genetic systems involved in the regulation of energy metabolism that may contribute to the development of obesity. These studies are reviewed in this section. The sodium-potassium ATPase system has been examined as a possible causative factor for reducing resting metabolic rate because of its inherent involvement in energy metabolism. The sodium-potassium ATPase provides the energy to transport positive ions across the cell membrane. The exact contribution of sodium-potassium ATPase activity to resting metabolic rate varies according to animal species and is different among the various organs of the body. In humans, sodiumpotassium ATPase activity accounts for ~ 10% of the variation in resting metabolic rate after removing the effects of fat free mass (Poehlman et al., 1993b). In addition, an age-related reduction in sodium-potassium ATPase has been shown to contribute to the age-related reduction in resting

Fig. 1. Similarity among young sib pairs of respiratory quotient, resting metabolic rate, and total energy expenditure. Top: Sib-pair correlation for RQ (respiratory quotient); RQ was adjusted for fat and fat-free mass; the residuals for Sib 1 were then correlated against the residual for Sib 2. The correlation is significant (r = .5, p = .002). Filled circles represent African American children, open circles represent Caucasian children, and open triangles represent Mohawk children. Middle: Sib-pair correlation for RMR (resting metabolic rate); RMR was adjusted for fat free mass; the residuals for Sib 1 were then correlated against the residual for Sib 2. The correlation is significant (r = .41, p =.01). Filled circles represent African American children, open circles represent Caucasian children, and open triangles represent Mohawk children. Bottom: Sib-pair correlation for TEE (total energy expenditure); TEE was adjusted for resting energy expenditure; the residuals for Sib 1 were then correlated against the residual for Sib 2. The correlation is significant (r = .33, p = .05). Filled circles represent African American children, open circles represent Caucasian children, and open triangles represent Mohawk children.

metabolic rate (Poehlman et al., 1993b). Moreover, when 12 healthy humans were treated with digoxin (an inhibitor of sodium-potassium ATPase), there was a significant 4% reduction in resting metabolic

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rate and a specific effect on reducing fat oxidation (Lyon et al., 1995). Thus, there is fairly good evidence to suggest that variation in the sodium-potassium ATPase system may influence energy and substrate metabolism in humans. The sodium-potassium ATPase is composed of a catalytic unit (with three isoforms) and another unit of unknown function. Five RFLPs at three of the sodium-potassium ATPase genes have been used to examine linkage with resting metabolic rate and substrate utilization (Deriaz et al., 1994). There was no significant relationship between the RFLPs and the variation in resting metabolic rate, but there was evidence of variation in the degree of fat versus carbohydrate utilization being associated and linked with the different genes encoding for sodium-potassium ATPase (Deriaz et al., 1994). The results suggested that DNA variation explained ~5% of the variation in adjusted substrate utilization, and the effect was due to an association with the genes coding for the a 2 subunit (the catalytic unit expressed mainly in muscle) and a linkage with the B subunit (which has an unknown function). These two genes are located on the long arm of chromosome 1, which contains two other genes that could potentially influence substrate utilization (phosphofructokinase and pyruvate kinase). Variation in the genes coding for the uncoupling protein have also been examined as a potential mechanism for the genetic influence on energy expenditure. The uncoupling protein is typically found on the inner mitochondrial membrane of brown adipose tissue. The uncoupling protein has long been examined for its role in the regulation of heat production because of its unique ability to generate heat in the absence of ATP synthesis. In animal models of obesity, a defect in uncoupling protein activity, has been hypothesized to be a causative factor through its effects on reducing energy expenditure (Trayhum, 1990; Rothwell and Stock, 1988). The role of uncoupling protein in adult humans has always been controversial. Recently, mRNA for the uncoupling protein has been found in human adipose tissue (Bouillaud et al., 1988), and a gene on the long arm of chromosome 4 has been identified, cloned (Cassard et al., 1990), and found to have two alleles (frequency of 0.28 and 0.72). However, in a sib-pair study (Oppert et al., 1994), there was no linkage between the marker alleles and the resting metabolic rate or body fat (or change in resting metabolic rate or body fat over 12 years).

An increase in resting energy expenditure has been observed in cystic fibrosis (Vaisman et al., 1987; Shepherd et al., 1988) and this increase in energy demands is speculated to contribute to wasting. Some studies have shown that resting metabolic rate is higher in those subjects homozygous for the cystic fibrosis mutant (O'Rawe et al., 1992), suggesting a possible genetic link. However, Fried et al. (1991) examined whether the increase in resting metabolic rate was related to the major gene defect (deltaF508) or to the degree of disease progression. They found that the increase in resting metabolic rate was related more to the decline in pulmonary function than to the DNA variation at the cystic fibrosis locus. Linkage has been reported between insulin action in Pima Indians and a region of DNA on chromosome 4q located close to the genetic locus for the intestinal fatty acid binding protein (FABP2). A polymorphism in the FABP2 gene results in two alleles (alanine versus threonine encoding; 71 versus 29% frequency). The threonine allele was associated with altered insulin action as well as a higher rate of fat oxidation in vivo, probably due to its stronger affinity for binding long-chain fatty acids, but its effect on energy expenditure was not reported (Baier et al., 1995). In summary, only a few studies have examined potential genetic linkage to variation in human energy expenditure. These few studies are beginning to suggest that the genetic influence on energy expenditure may be transmitted through genetic effects on the ability to metabolize different substrates. SUMMARY Energy expenditure is the net result of the energy released through the controlled metabolic combustion of the substrate fat, carbohydrate, protein, and alcohol that are either ingested or are already stored in the body. Studies of genetic susceptibility to obesity suggest that altered energy expenditure and/or preferential substrate utilization are likely to be involved in the etiology of obesity. However, only a few studies have examined the underlying genetic aspects of energy expenditure and/or substrate utilization. Such studies are confounded by the fact that both energy expenditure and substrate utilization are influenced predominantly by fat-free mass, which is itself likely to be


genetically influenced. Twin and family studies suggest that there is a strongly heritable component to resting energy expenditure (with correlations of .20-.40 in DZ twins and sib pairs to —.80 in MZ twins) and substrate utilization (.25-30 in parentchild and/or sib pairs) and the thermic response to feeding (.40-.50 in parent-child pairs and DZ twin pairs to 70% based on the response to overfeeding in DZ twins). Physical activity energy, expenditure has been less well studied due to measurement difficulties, and new data are presented in young sib pairs that suggest that the correlation between sibs for total energy expenditure adjusted for resting energy expenditure (.11) may be lower than resting energy expenditure adjusted for body composition (.17) and substrate utilization adjusted for body composition (.25). Only a few candidate gene studies have been performed to examine the role of basic proteins involved in energy expenditure (the sodium-potassium ATPase and the uncoupling protein) or substrate utilization (fatty acid binding protein). The lack of knowledge in this area suggests that future studies are warranted to examine the underlying genetic aspects involved in the regulation of energy and substrate metabolism.

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