(resistant) vs obese (susceptible) - Nature

European Journal of Clinical Nutrition (2007) 61, 166–174

& 2007 Nature Publishing Group All rights reserved 0954-3007/07 $30.00 www.nature.com/ejcn

ORIGINAL ARTICLE

Differences in short-term metabolic responses to a lipid load in lean (resistant) vs obese (susceptible) young male subjects with habitual high-fat consumption MP Marrades, JA Martı´nez, MJ Moreno-Aliaga Department of Physiology and Nutrition, University of Navarra, Pamplona, Spain

Objective: To determine the role of macronutrients oxidation ability in the postprandial response to a high lipid load in the mechanisms conferring resistance or susceptibility to obesity. Subjects and design: Nine lean and nine obese young male subjects with habitual similar high-fat intake (440 % of energy) and comparable physical activity were selected and categorized as ‘resistant’, those who remained lean (body mass index (BMI) o25 kg/m2), and ‘susceptible’, those who were obese (BMI430 kg/m2). Fasting blood samples were taken for the evaluation of blood metabolic and hormonal variables. Resting metabolic rate (RMR) and substrates oxidation were measured by indirect calorimetry, in the fasting state and every 30 min for 3 h after a rich lipid meal (fat 94.7%) supplied to cover the 50% of the volunteers energy requirements. The study was performed at the Metabolic Unit of the University of Navarra. Results: Fasting RMR and lipid oxidation were higher in obese–susceptible subjects. However, similar values were found in both groups after adjustment for fat mass and free fat mass. The cumulative postprandial fat oxidation was also similar in both groups (despite having different tissue metabolic activity), whereas cumulative carbohydrate oxidation was lower in the obese– susceptible group. The thermic effect of food (% of dietary induced thermogenesis) was lower (Po0.05) in the susceptible– obese subjects. The energy and fat balance were more positive in the obesity-susceptible individuals after the high fat load, who also showed higher fasting homeostatic model assessment index, low-density lipoprotein-cholesterol and triacylglyceride levels, hyperleptinemia and hypoadiponectinemia. Conclusion: Lean–resistant individuals came closer to achieving fat balance than obese–susceptible subjects. These metabolic and hormonal differences are probably genetically determined.

European Journal of Clinical Nutrition (2007) 61, 166–174. doi:10.1038/sj.ejcn.1602500; published online 9 August 2006 Keywords: energy metabolism; fat oxidation; obesity; predisposition; human

Introduction Obesity is a multifactorial and complex disease that develops from an interaction of genotype and environmental factors related to excessive food intake and sedentary behaviors (Martinez, 2000). Stability of body weight and composition requires that over time, energy intake equals energy expenditure and also that intakes of protein, carbohydrate Correspondence: Dr MJ Moreno-Aliaga, Department of Physiology and Nutrition, University of Navarra, 31008 Pamplona, Spain. E-mail: [email protected] Guarantor: JA Martı´nez. Received 28 May 2005; revised 15 December 2005; accepted 17 June 2006; published online 9 August 2006

and fat equal the oxidation of each (Flatt, 1987; Acheson et al., 1988). The Western diet has increased energy density and fat, and hence when a similar volume of food is consumed, energy intake will be higher on high-fat diets compared with low-fat diets. (Westerterp-Plantenga et al., 1996). Short- and medium-term studies show that, unlike carbohydrate and protein intake, fat intake does not promote its own oxidation (Schutz, 1995). Consequently, carbohydrate and protein balances seem promptly regulated, whereas fat balance is not acutely adjusted. Thus, a dietary ‘excess’ of fat intake results more easily in fat deposition. As fat mass increases, so does fat oxidation, and a new equilibrium is reached when fat oxidation matches fat intake (Prentice et al., 1994). However, there are large

Energy metabolism and predisposition to obesity MP Marrades et al

167 interindividual differences in this compensatory response to increased fat intake. Indeed, the stability of body weight and its associated regulatory processes are influenced by compensatory genetics-dependent metabolic and neuroendocrine mechanisms (Marti et al., 2004). An early epidemiological evidence for a relationship between fat intake and obesity was reported (Lissner and Heitmann, 1995). Of course, one consequence of a positive balance induced by a continuous exposure to high-fat foods should be an increase in body weight. The Leeds high-fat study (Macdiarmid et al., 1996) found 19 times more obese in the high-fat than in the low-fat group. However, not everyone in the sample identified as eating a high-fat diet was obese. Indeed, there were many normal-weight and even some underweight people among the high-fat consumers. This suggests that some people are able to resist the weightincreasing properties of high-fat diets, whereas some people are susceptible. Furthermore, studies in animals have revealed that some strains of mice and rats are susceptible to develop obesity when eating high-fat diets, whereas other strains, when fed similar diets, are resistant (Perez-Echarri et al., 2005). Whether the animals are susceptible or resistant to obesity when eating high-fat diet has a strong genetic component (Bray et al., 2004). It has been suggested that the resistance to obesity of some high-fat consumers could be explained by spending of extremely high levels of energy in physical activity. In fact, high levels of physical activity facilitate long-term weight control (Jeffery et al., 2003; Achten and Jeukendrup, 2004; Wadden et al., 2004). On the other hand, it has been suggested that some individuals may be resistant to dietinduced obesity because of their high resting metabolic rate (RMR), and that other subjects may be prone to diet-induced obesity because of their lower RMRs (Ravussin, 1995; Astrup et al., 1996; Blundell and Macdiarmid, 1997). However, the role of RMR in the susceptibility to obesity is controversial because obesity is usually associated with high absolute metabolic rate (Leibel et al., 1995; Ravussin and Gautier, 1999). The ability to avoid dietary obesity produced by a high-fat diet may also be related with high rates of fat oxidation (Chang et al., 1990). In this sense, Zurlo et al. (1990) found that low-fat oxidizers exhibited a higher risk of gaining weight in comparison to high-fat oxidizers, whereas Thomas et al. (1992) reported an increase in fat oxidation in lean but not in obese subjects 7 days after switching to a high-fat diet. Furthermore, Giacco et al. (2004) showed that normal-weight subjects with a strong family history of obesity had a reduced lipid oxidation in the postprandial period after a high-fat meal. In summary, it has been suggested that all these factors, RMR, fat oxidation and physical activity, interact with each other and this would potentially explain why some individuals show differential susceptibility to weight gain on highfat intakes. In the present study, we have recruited two groups of subjects who despite living in the same environmental conditions (habitual high-fat dietary intake and

similar moderate physical activity), were successfully ‘resistant’ (lean) to gain weight, whereas others were obesity ‘susceptible’. A major feature of this trial was to determine the role of RMR, substrates oxidation ability and the postprandial response to a rich lipid meal under isoenergetic conditions in the mechanisms conferring resistance or susceptibility to obesity.

Subjects and methods Subjects, food intake and physical activity pattern In order to recruit subjects, a validated questionnaire (Sanchez-Villegas et al., 2003) based on self-reported questions about lifestyle and food frequency was mailed to more than 100 young male adults. Before the participation in the study, the enrolled volunteers with a high-fat intake and similar physical activity underwent a complete physical and medical examination. All subjects were healthy, nondiabetic, non-hyperlipidemic, taking no oral medications and with stable body weight during the previous 3 months. Eighteen high-fat consumers were selected and again asked to record all food eaten by a 3-day weighted food questionnaire of 2 weekdays and 1 weekend day, which was analyzed by a computerized program by a trained nutritionist (Medisystem, Sanocare, Madrid, Spain). Moreover, each participant completed a validated questionnaire about their leisure time physical activities and their work-time physical activity on a typical workday and on a typical weekend day. Metabolic equivalents (METs) were assigned to each activity and a composite value for total METs (h/week per participant) was accordingly computed as described previously (Martinez-Gonzalez et al., 1999). Despite living in the same environmental conditions (concerning fat intake and physical activity), a group of subjects remained lean (n ¼ 9) whereas the other group (n ¼ 9) was obese. So it was assumed that lean were ‘resistant’ and obese were ‘susceptible’ to dietary-induced obesity. The groups were matched by age (range, 22–33 years old for lean and 21–35 years old for the obese group). The protocol was approved by the Ethical Committee of the University of Navarra at the University Clinic, and all subjects gave their written informed consent before participating in the study.

Experimental design On the day of the intervention study, volunteers arrived at the Metabolic Unit of the Navarra University after 12 h of overnight fast. Anthropometric measurements were made by standard procedures as described previously (Labayen et al., 2004) and body composition was measured by bioelectrical impedance (Qadscan 4000, Bodystat, UK). Volunteers rested supine for 30 min (considered to be an adaptative period), and basal respiratory exchange measurements were determined by indirect calorimetry (Deltatrac, European Journal of Clinical Nutrition


168 Datex-Ohmeda, Finland) as described elsewhere (MarquesLopes et al., 2001, 2003). A catheter was then inserted into the antecubital vein for a fasting blood sample extraction. Thereafter, all participants ingested a rich lipid meal and postprandial changes in energy expenditure and in the oxidation of the different substrates were estimated from gas exchange measurements at 30-min intervals by using the constants of Elia and Livesey (1992) during the 3 h postprandial period. The rate of protein oxidation was assumed to be constant throughout the test as described by Raben et al. (2003).

Test meal The composition of the test meal was 94.7% of energy from saturated fat, 2.9% from carbohydrates and 2.4% from proteins. The subjects’ energy requirements were determined by using World Health Organization (WHO) tables according to age, weight and sex. Each subject received the amount of 50% of their energy requirements as a breakfast (isoenergetic conditions). The mean of energy intake was 3717.4752.1 kJ for lean–resistant vs 4628.3729.2 kJ for obese-susceptible volunteers.

Blood measurements Blood samples were collected, immediately centrifuged and then stored at 801C. Plasma and serum metabolic assays were performed using commercially available methods on a Cobas Mira autoanalyzer: fasting serum glucose, plasma lipid profiles including total cholesterol, high-density lipoprotein (HDL) cholesterol and triacylglycerides (ABX Roche, Geneva, Switzerland); free fatty acids (FFA) (Wako Chemicals, Neuss, Germany); glycerol and b-hydroxybutyrate (Randox Laboratories, Co. Antrim, UK). Serum adiponectin (Linco Research Inc., St Charles, MO, USA), insulin (Coat-A-Count Insulin, Diagnostic Products Corporation, Los Angeles, CA, USA) and leptin levels (Human Leptin IRMA, DSL, Webster, TX, USA) were determined by commercial radioimmunoassay kits. Insulin resistance was indirectly determined by the homeostatic model assessment index (HOMA), as the product of fasting insulinemia (mUI/ml) per glycemia (mM), which was divided per 22.5 (Matthews et al., 1985).

calculated as the difference between the quantities of energy ingested in the breakfast minus the quantity of energy consumed during the postprandial period (AUCI of energy expenditure (EE)). The lipid balance (fat input minus fat output) was calculated as the difference between quantities of ingested energy as fat minus AUCI of fat oxidation as defined elsewhere (Schutz, 2004). Data were analyzed by either unpaired Student’s t-test or Mann–Whitney U-test, as appropriate, depending on the results of the Kolmogorov– Smirnoff and Shapiro–Wilk normality tests. Differences between groups, time–effect and time–group interaction were analyzed with mixed linear models for repeated measures analysis of variance (ANOVA) for the postprandial indirect calorimetry values. The SPSS 11.0 version for WINDOWS was used for the statistical analysis.

Results Subjects’ characteristics As designed, subjects with habitual high-fat intake and similar physical activity were divided into two groups (n ¼ 9) according to their body mass index (BMI) (lean or obese). Table 1 shows the phenotypical characteristics of both groups. As expected, obese individuals (BMI430 kg/m2) had higher body FM, body FFM, and also a higher waist-tohip ratio than lean subjects. The lean group was slightly younger than the obese, but not significant differences were found. The habitual food intake and physical activity characteristics of the 18 young males are described in Table 2. The analysis of the 3-day weighted food records showed that both groups (lean and obese) had a similar eating pattern with respect to the calorie intake and the energy distribution of macronutrients (carbohydrate, protein and fat). In addition, both groups had a high percentage of fat in the diet (440% of energy), with the same proportion of different types of dietary fat (saturated, monounsaturated and polyunsaturated). Regarding the physical activity, evaluated by the calculation of sport-METs (h/week), both groups reported

Table 1

Calculations and data analysis All data are presented as means7s.e.m. RMR and fasting substrates oxidation values were adjusted for FFM (free fat mass) and FM (fat mass) by general linear regression models. The postprandial incremental area under the curve (AUCI) was calculated using the trapezoidal method above the fasting values. The dietary thermogenic (% dietary-induced thermogenesis (DIT)) effect was calculated as the difference between energy expenditure after food consumption and basal energy expenditure, divided by the total energy ingested (Bendixen et al., 2002). The energy balance was European Journal of Clinical Nutrition

Physical characteristics of the participants in the study

Weight (kg) Height (m) BMI (kg/m2) Waist/hip ratio FFM (kg) FM (kg) Body fat (%)

Resistant (n ¼ 9)

Susceptible (n ¼ 9)

P-value

71.671.5 1.7670.01 23.170.4 0.8770.01 60.571.4 10.370.7 14.470.9

108.272.9 1.7770.01 34.771.2 0.9470.03 75.771.4 32.572.3 29.871.2

0.000 0.675 0.000 0.019 0.000 0.000 0.000

Abbreviations: FM, fat mass; FFM, free fat mass. Values are means7s.e.m. Independent Student’s t-test or Mann–Whitney U-test was performed, as appropriate, depending on the results of the Kolmogorov–Smirnoff and Shapiro–Wilk normality tests.


169 Table 2 Daily energy and macronutrient distribution intake and physical activity level of the obese-susceptible and lean-resistant subjects Resistant (n ¼ 9)


P-value

Energy (kcal) Fat (g) Saturated Monounsaturated Polyunsaturated

2766.77258.7 137.0717.3 38.575.7 83.5710.5 15.072.3

2799.17171.4 132.1711.6 32.873.2 80.278.9 19.173.9

0.918 0.818 0.395 0.811 0.375

Carbohydrate (g) Complex Simple Dietary fiber (g) Protein (g) Fat (% E) Carbohydrate (% E) Protein (% E) METs (h/week)a

256.2725.4 120.3716.5 116.3713.6 18.772.7 108.177.2 44.672.2 37.076.9 15.671.1 17.575.1

271.6711.6 126.6714.4 96.7715.1 14.871.7 126.879.6 42.571.8 38.871.6 18.170.9 18.074.4

0.590 0.778 0.346 0.245 0.140 0.573 0.619 0.200 0.945

Abbreviations: E, energy; METs, metabolic equivalents. Values are means7s.e.m. Independent Student’s t-test or Mann–Whitney U-test was performed, as appropriate, depending on the results of the Kolmogorov–Smirnoff and Shapiro–Wilk normality tests. a Leisure-time physical activity.

a similar moderate physical activity in their free-living time (Table 2).

Biochemical parameters The susceptible–obese subjects had higher plasma glucose (marginally significant) and significantly higher serum insulin concentrations than the lean–resistant individuals. Furthermore, the HOMA index was significantly higher in the obese–susceptible than in the lean–resistant (even it was still in the normal range; o3.5), suggesting that obese– susceptible subjects have a higher risk for developing insulin resistance (Table 3). Fasting plasma lipid profiles including total cholesterol, LDL cholesterol, HDL cholesterol and triacylglycerides were also within the normal range in both groups (Table 3). However, the obese–susceptible men had significantly higher plasma levels of total cholesterol, LDL-cholesterol and triglycerides, whereas lower values of HDL-cholesterol (marginally significant) than lean–resistant volunteers. No marked changes were observed in the plasma levels of FFA, glycerol and b-hydroxybutyrate (Table 3). With regard to the basal hormonal profile, the obese– susceptible individuals presented higher plasma levels of leptin, in correlation with their adiposity. On the opposite, the levels of adiponectin were statistically significantly lower in the obese–susceptible individuals.

Fasting energy expenditure measurements RMR, non-protein respiratory quotient (NPQR) and fasting substrate oxidation were measured in both experimental groups (Table 4). RMR was significantly higher in the obese– susceptible subjects. However, when this parameter was

Table 3 Fasting metabolic and hormonal characteristics of obesesusceptible and lean-resistant volunteers Resistant (n ¼ 9) Susceptible (n ¼ 9) P-value Glucose (mg/dl) Insulin (mUI/ml) HOMA Leptin (ng/ml) Adiponectin (mg/ml) Total cholesterol (mg/dl) HDL cholesterol (mg/dl) LDL cholesterol (mg/dl) Triglycerides (mg/dl) FFA (mmol/dl) Glycerol (mmol/l) b-Hydroxybutyrate (mmol/l)

90.373.9 3.670.1 0.7870.03 8.372.6 20.173.3 167.4717.7 43.371.7 124.1717.0 85.076.7 0.3970.03 322.2727.4 0.0770.03

92.772.3 10.573.1 2.3270.66 33.374.6 10.071.7 188.576.3 40.072.4 148.577.0 142.2710.6 0.4470.05 351.6724.6 0.0570.01

0.070 0.001 0.001 0.000 0.037 0.008 0.059 0.010 0.001 0.226 0.112 0.728

Abbreviations: HOMA, homeostatic model assessment index; HDL, highdensity lipoprotein; LDL, low-density lipoprotein; FFA, free fatty acids. Values are means7s.e.m. Independent Student’s t-test or Mann–Whitney U-test was performed, as appropriate, depending on the results of the Kolmogorov–Smirnoff and Shapiro–Wilk normality tests.

adjusted for FM and FFM, no differences between groups were found. Obese–susceptible subjects had a significantly lower NPQR. Basal lipid oxidation was also significantly elevated in this obese–susceptible group. On the contrary, carbohydrate oxidation was marginally decreased in obese– susceptible volunteers. However, after adjusting for FM and FFM, no differences between both groups were found either in fat or carbohydrate oxidation values. Regarding protein oxidation, both lean and obese groups exhibited similar values before and after the adjustment (Table 4).

Postprandial measurements The postprandial patterns for energy expenditure, lipid and carbohydrate oxidation were analyzed during 180 min after the intake of a rich fat meal. Figure 1a and b shows the postprandial fat and carbohydrate oxidation obtained for lean–resistant and obese–susceptible subjects. The ANOVA analysis of repeated measures did not reveal interactions between time and group in any analysis. Similar to findings on fasting conditions, after the high-fat load, obese had significantly higher lipid oxidation rates (effect of group; P ¼ 0.002) than lean–resistant volunteers. However, no significant changes were observed in postprandial cumulative fat oxidation between the two groups (5.070.5 vs 6.272.1; P ¼ 0.562). Carbohydrate oxidation was lower (effect of group; P ¼ 0.032) in the obese–susceptible group along the time. The carbohydrate oxidation became negative after the high-fat meal in both groups, but the postprandial cumulative carbohydrate oxidation decrease was more dramatic (Po0.05) in obese than in lean–resistant subjects (Figure 1b). The thermogenic effect of food expressed as percentage of DIT was significantly lower (Po0.05) in the susceptible– obese subjects (Figure 2). Moreover, resistant individuals came closer to achieving energy and fat balance than European Journal of Clinical Nutrition


170 Table 4 Resting metabolic rate (RMR), substrate oxidation and non-protein respiratory quotient (NPQR) measurements by indirect calorimetry before the high-fat meal

RMR (kJ/min) RMRFFM&FM (kJ/min)a NPQR Lipid oxidation (mg/min) Carbohydrate oxidation (mg/min) Protein oxidation (mg/min) Lipid OxFFM&FM (mg/min)a CHO OxFFM&FM (mg/min)a Protein OXFFM&FM (mg/min)a

Resistant (n ¼ 9)


P-value

5.0370.12 5.5970.97 0.8370.02 56.8576.75 94.64711.22 60.8174.06 70.6275.80 90.66710.59 68.6874.47

6.1870.14 5.6170.13 0.7770.01 95.2176.37 63.08714.94 71.3175.39 83.8077.28 65.61710.67 65.2474.93

0.000 0.905 0.015 0.001 0.085 0.140 0.177 0.185 0.613

Abbreviations: CHO, carbohydrate; FFM, free fat mass; FM, fat mass; NPQR, non-protein respiratory quotient; RMR, resting metabolic rate. Values are means7s.e.m. Independent Student’s t-test or Mann–Whitney U-test was performed, as appropriate, depending on the results of the Kolmogorov– Smirnoff and Shapiro–Wilk normality tests. a Means were adjusted by fat mass and free fat mass with the linear regression model.

CHO oxidation (mg /min)

b

120 80 40 0

30

60

90 t (min)

120

150

180

AUCI of LIPID oxidation

160

15 13 11 9 7 5 3 1

Resistant

Susceptible

120 100

AUCI of CHO oxidation

LIPID oxidation (mg /min)

a

80 60 40 20 0 -20 0

30

60

Resistant

90 t (min)

120

150

180

Resistant -1 -3 -5 -7 -9 -11 -13 -15

Susceptible

*

Susceptible

Figure 1 (a and b) Postprandial fat and carbohydrate oxidation, respectively, obtained for lean–resistant and obese–susceptible subjects. Data are means7s.e.m. (n ¼ 8) for each group. The ANOVA analysis of repeated measures did not reveal interactions time and group in any analysis. (a) Left panel: repeated measures ANOVA of lipid oxidation rates showed a significant group effect (P ¼ 0.002) and a significant time effect (Po0.001); right panel: the incremental area under curve (IAUC) of fat oxidation during the postprandial period after the high fat load. (b) Left panel: repeated measures ANOVA of carbohydrate oxidation rates showed a significant group effect (P ¼ 0.032) and a significant time effect (Po0.001); right panel: the IAUC of carbohydrate oxidation during the postprandial study (*Po0.05).

susceptible individuals and statistically significant differences (Po0.001) between the two groups were observed concerning this issue (Figure 3).

Discussion Two groups of subjects with different susceptibility to highfat-induced obesity were identified. In fact, despite having European Journal of Clinical Nutrition

habitual similar energy intake (with similar high proportion of energy as fat 440%) and similar patterns of physical activity, one group of volunteers seemed to be resistant to obesity and remained lean, whereas the other group was susceptible and developed obesity. The study of Blundell and Macdiarmid (1997) also suggests that some people are able to resist the weight-increasing properties of high-fat diets. Our data also indicate that this resistance to obesity is not owing to higher EE in physical activity. Several reports have


171 6 5 *

% D.I.T

4 3 2 1 0 Resistant

Susceptible

Figure 2 %DIT in resistant–lean and susceptible–obese subjects after the rich lipid meal. Values (means7s.e.m) are expressed in percentage, n ¼ 8 for each group. Mann–Whitney U-tests between obese and lean were performed (*Po0.05).

5000

***

***

KJ

4000 3000 2000 1000 0 Energy balance Lean-resistant

Lipid balance Obese-susceptible

Figure 3 Energy and lipid balance in resistant–lean and susceptible–obese individuals after the ingestion of the rich lipid meal. Values are means7s.e.m. n ¼ 8 for each group. Independent Student’s t-test between obese and lean was performed. The energetic balance was calculated as the difference between the quantity of energy in the breakfast minus the quantity of energy consumed during postprandial period (AUCI of EE). The lipid balance was calculated as the difference between the quantity of energy ingested as fat minus AUCI of fat oxidation (***Po0.001).

suggested that obesity is usually associated with lower physical activity, and that obese subjects tend to overestimate their physical activity (Lichtman et al., 1992; Johansson et al., 1998). In order to avoid this limitation, we included in the study those moderate physically active young healthy obese and discarded for the study lean subjects with an intense physical activity. Another possible limitation to be taken into account when planning the study was the possibility of under-reporting of energy intake, especially in obese subjects (Kunz et al., 2002). The possibility of under-reporting was evaluated by the calculation of the ratio EI (energy intake)/BMR (basal metabolic rate) according to Johansson et al. (1998) and Kunz et al. (2002). Values of EI/BMR within 1.35–2.39 are considered to be in the normal range and values o1.34 are considered as under-reporting (Johansson et al., 1998). For our lean volunteers, the mean ratio of EI/BMR was 1.75 and for the obese group was 1.43, suggesting that our volunteers were in acceptable ranges of energy intake reporting.

Previous studies have suggested that some individuals may be resistant to diet-induced obesity because of their high RMR, and that other subjects may be prone to diet-induced obesity because of their lower RMRs (Ravussin, 1995; Astrup et al., 1996; Blundell and Macdiarmid, 1997). However, in agreement with the data obtained in our study, other trials have shown that obesity is usually associated with a high absolute metabolic rate because of the higher mass in obese subjects (Leibel et al., 1995; Ravussin and Gautier, 1999). In fact, when RMR was adjusted by both FM and FFM, comparable values were obtained for lean–resistant and obese–susceptible subjects, suggesting that alterations in RMR are not involved in the susceptibility or resistance to obesity by a high-fat intake in the experimental groups. The ability to avoid dietary obesity produced by a high-fat diet may be also related with high rates of fat oxidation (Chang et al., 1990). Zurlo et al. (1990) found that low-fat oxidizers exhibited a higher risk of gaining weight in comparison to high-fat oxidizers. However, our data reveal that obese–susceptible subjects have higher fasting lipid oxidation rates than lean–resistant individuals. This is in agreement with previous reports, which showed a positive correlation between body FM and fasting fat oxidation (Schutz et al., 1992; Astrup et al., 1994; Kunz et al., 2002), as explained by the model proposed by Flatt (1987). Thus, consumption of high-fat diets will lead to a positive fat balance and therefore an increase in FM will occur. This increase is then accompanied by increased fat oxidation thereby compensating for the high-fat intake. This could explain why the obese–susceptible subjects have higher rates of fat oxidation and lower NPRQ than lean–resistant individuals. Indeed, when fat oxidation was adjusted for FM and FFM, similar levels were found in lean subjects and in obese subjects. Therefore, according to Flatt’s model, the increase in fat oxidation in obese–susceptible subjects consuming high-fat diets would maintain the glycogen concentrations in a lower range, which is compatible with our finding that carbohydrate oxidation rate was marginally lower in obese than in lean habitual high-fat intakers, as described previously by Kunz et al. (2002). As the differences in the status of glycogen stores are highly dependent on the diet and the physical exercise performance, all subjects recruited were weight stable and remained on their habitual high-fat intake and avoided any intense physical activity on the days preceding the trial. Previous studies have shown that carbohydrate balance became negative during the first days of high-fat feeding (Schrauwen et al., 2000; Smith et al., 2000). Similarly, our data show a negative postprandial carbohydrate oxidation in both lean and obese subjects after the high-fat load (50% of energy estimated requirements). However, this decrease in postprandial carbohydrate oxidation was higher in the obese than in the lean group. It has been proposed that one component of the adaptation to energy-dense foods may be the rate at which carbohydrate oxidation is inhibited and the rate at which fat oxidation can be increased (Smith et al., 2000). Our data raised the question European Journal of Clinical Nutrition


172 about the contribution of a decreased carbohydrate oxidation to the accumulation of fat by promoting fat synthesis, especially in obese subjects. A previous study of our group showed that overweight men had a lower postpandrial fat oxidation together with a higher postpandrial lipogenesis after a high-carbohydrate meal (Marques-Lopes et al., 2001). In the present trial, we did not evaluate postpandrial lipogenesis; however, the fact that NPRQ remained lower than 1 in both lean and obese subjects suggests that there was no positive postpandrial net lipogenesis in any experimental group. Our study suggests that obese subjects have reduced carbohydrate oxidation rate in order to promote fat oxidation. However, after the rich lipid load, there were no significant differences in the increase in the fat oxidation between obese and lean subjects, despite obese subjects having a higher metabolic active mass. These similar rates of postprandial fat oxidation suggest that obese individuals have a difficulty in properly adjusting the amount of fat intake to the amount of fat oxidized. Therefore, our data indicate that less fat will be oxidized and more fat will be stored after a high-fat meal in obese–susceptible subjects. In this way, the study performed by Giacco et al. (2004) showed that normal-weight subjects with a strong family history of overweight had a reduced lipid oxidation in the postprandial period after a high-fat meal. In fact, the differences between the fat ingested vs the fat oxidized were significantly higher in obese than in lean individuals, and therefore, obese– susceptible subjects present a more positive fat balance compared with lean–resistant individuals. In addition, %DIT values, one of the three components of daily EE that could play a role in the development of obesity, remained lower in the obese–susceptible subjects over 3 h. The study of Reed and Hill (1996) concluded that DIT is a response lasting more than 6 h, especially in obese subjects. However, Segal et al. (1990b) reported that 70% of the thermic effect of food occurs within 3 h after the meal, arguing that measuring this 70% of the response is sufficient for comparative purposes. Our results are in agreement with the majority of studies that used a postpandrial measurement period p3 h, which reported that the thermic effect of food is reduced in obesity (Segal et al., 1990a, b; De Jonge and Bray, 1997, 2002; Granata and Brandon, 2002a, b). This trial gives support to the fact that resistance/ susceptibility to weight gain when eating high-fat diets may be linked to the genetic background (Moreno-Aliaga et al., 2005). The differences in the metabolic response to a high-fat diet between resistant–lean and obese–susceptible may be a result of genetic factors that influence nutrient partitioning by influencing the activity of key enzymes of intermediate metabolism and/or the hormonal regulation, which could alter the influence of the body’s fat stores in promoting fat relative to glucose oxidation. In this way, some hormones produced by the adipose tissue seem to play a critical role in the regulation of food intake, EE and lipid and carbohydrate metabolism (Havel, European Journal of Clinical Nutrition

2004). Indeed, the leptin system seems to be involved in the diminished ability to maintain fat balance in obese individuals. In agreement with our results, it is well known that most of the obese human subjects exhibit high levels of circulating leptin, suggesting that obesity is not caused by a leptin deficiency per se, but it is accompanied by leptin resistance (Auwerx and Staels, 1998). As described previously, obese–susceptible individuals exhibit hypoadiponectinemia, whereas the administration of this hormone in rodents produces an increase in fatty acid oxidation (Fruebis et al., 2001) and helps to lose weight by increasing EE, without affecting food intake (Qi et al., 2004). These observations suggest that the lower %DIT and the difficulty in achieving fat and energy balance observed in susceptible subjects could be related with their hypoadiponectinemia, although several studies have suggested that the drop in adiponectin levels seems to be more likely a consequence than a cause of obesity (Maeda et al., 2002; Stefan et al., 2002). In addition, our data also support previous studies about the role of adiponectin deficiency in the development of insulin resistance (Hotta et al., 2001; Kubota et al., 2002) and cardiovascular risk associated with obesity (Kumada et al., 2003; Pischon et al., 2004). This outcome is in accordance with the higher values of HOMA index, triglycerides and LDL cholesterol in the fasted condition of obese–susceptible compared to lean–resistant subjects. Therefore, the fact that obese–susceptible subjects have higher triglycerides and LDL-cholesterol values despite a similar energy and fat intake may be explained because such individuals have higher fat depots, which seems to be owing to a genetic predisposition (Marrades et al., 2006), being not apparently linked to the lipid intake. In summary, our data suggest that lean–resistant individuals came closer to achieving fat and energy balance than obese–susceptible subjects. The underlying metabolic and hormonal mechanisms are probably genetically determined. Therefore, it can be concluded that the development of obesity on a high-fat diet is not a biological inevitability and confirms that human obesity-susceptible or obesity-resistant genotypes may exist. Further progress in the genetic basis of this susceptibility will contribute to clarify this issue.

Acknowledgements We thank the Government of Navarra (Health Department) for supporting this work included in the Linea Especial of the University of Navarra (LE/97).

References Acheson KJ, Schutz Y, Bessard T, Anantharaman K, Flatt JP, Jequier E (1988). Glycogen storage capacity and de novo lipogenesis during massive carbohydrate overfeeding in man. Am J Clin Nutr 48, 240–247.


173 Achten J, Jeukendrup AE (2004). Optimizing fat oxidation through exercise and diet. Nutrition 20, 716–727. Astrup A, Buemann B, Christensen NJ, Toubro S (1994). Failure to increase lipid oxidation in response to increasing dietary fat content in formerly obese women. Am J Physiol 266, 592–599. Astrup A, Buemann B, Toubro S, Ranneries C, Raben A (1996). Low resting metabolic rate in subjects predisposed to obesity: a role for thyroid status. Am J Clin Nutr 63, 879–883. Auwerx J, Staels B (1998). Leptin. Lancet 7, 737–742. Bendixen H, Flint A, Raben A, Hoy CE, Mu H, Xu X et al. (2002). Effect of 3 modified fats and a conventional fat on appetite, energy intake, energy expenditure, and substrate oxidation in healthy men. Am J Clin Nutr 75, 47–56. Blundell JE, Macdiarmid JI (1997). Passive overconsumption. Fat intake and short-term energy balance. Ann NY Acad Sci 20, 392– 407. Bray GA, Paeratakul S, Popkin BM (2004). Dietary fat and obesity: a review of animal, clinical and epidemiological studies. Physiol Behav 30, 549–555. Chang S, Graham B, Yakubu F, Lin D, Peters JC, Hill JO (1990). Metabolic differences between obesity-prone and obesity-resistant rats. Am J Physiol 259, 1103–1110. De Jonge L, Bray GA (1997). The thermic effect of food and obesity: a critical review. Obes Res 5, 622–631. De Jonge L, Bray GA (2002). The thermic effect of food is reduced in obesity. Nutr Rev 60, 295–297. Elia M, Livesey G (1992). Energy expenditure and fuel selection in biological systems: the theory and practice of calculations based on indirect calorimetry and tracer methods. In: Simopoulus AP (ed). Metabolic Control of Eating, Energy Expenditure and the Bioenergetics of Obesity. World Review of Nutrition and Dietetics, vol. 70. Basel: Karger, pp 68–131. Flatt JP (1987). The difference in the storage capacities for carbohydrate and for fat, and its implications in the regulation of body weight. Ann NY Acad Sci 499, 104–123. Fruebis J, Tsao TS, Javorschi S, Ebbets-Reed D, Erickson MR, Yen FT et al. (2001). Proteolytic cleavage product of 30-kDa adipocyte complement-related protein increases fatty acid oxidation in muscle and causes weight loss in mice. Proc Natl Acad Sci USA 13, 2005–2010. Giacco R, Clemente G, Busiello L, Lasorella G, Rivieccio AM, Rivellese AA et al. (2004). Insulin sensitivity is increased and fat oxidation after a high-fat meal is reduced in normal-weight healthy men with strong familial predisposition to overweight. Int J Obes Relat Metab Disord 28, 342–348. Granata GP, Brandon LJ (2002a). The thermic effect of food and obesity: discrepant results and methodological variations. Nutr Rev 60, 223–233. Granata GP, Brandon LJ (2002b). Methodological variations and inconsistencies compromise the science of examining the thermic effect of food. Nutr Rev 60, 299–300. Havel PJ (2004). Update on adipocyte hormones: regulation of energy balance and carbohydrate/lipid metabolism. Diabetes 53 (Suppl 1), S143–S151. Hotta K, Funahashi T, Bodkin NL, Ortmeyer HK, Arita Y, Hansen BC (2001). Circulating concentrations of the adipocyte protein adiponectin are decreased in parallel with reduced insulin sensitivity during the progression to type 2 diabetes in rhesus monkeys. Diabetes 50, 1126–1133. Jeffery RW, Wing RR, Sherwood NE, Tate DF (2003). Physical activity and weight loss: does prescribing higher physical activity goals improve outcome? Am J Clin Nutr 78, 684–689. Johansson L, Solvoll K, Bjorneboe GE, Drevon CA (1998). Underand overreporting of energy intake related to weight status and lifestyle in a nationwide sample. Am J Clin Nutr 68, 266–274. Kubota N, Terauchi Y, Yamauchi T, Kubota T, Moroi M, Matsui J et al. (2002). Disruption of adiponectin causes insulin resistance and neointimal formation. Biol Chem 277, 25863–25866.

Kumada M, Kihara S, Sumitsuji S, Kawamoto T, Matsumoto S, Ouchi N et al. (2003). Association of hypoadiponectinemia with coronary artery disease in men . Arterioscler Thromb Vasc Biol 23, 85–89. Kunz I, Schorr U, Rommling K, Klaus S, Sharma AM (2002). Habitual fat intake and basal fat oxidation in obese and non-obese Caucasians. Int J Obes Relat Metab Disord 26, 150–156. Labayen I, Diez N, Parra D, Gonzalez A, Martinez JA (2004). Basal and postprandial substrate oxidation rates in obese women receiving two test meals with different protein content. Clin Nutr 23, 571– 578. Leibel RL, Rosenbaum M, Hirsch J (1995). Changes in energy expenditure resulting from altered body weight. N Engl J Med 9, 621–628. Lichtman SW, Pisarska K, Berman ER, Pestone M, Dowling H, Offenbacher E et al. (1992). Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. N Engl J Med 31, 1893–1898. Lissner L, Heitmann BL (1995). Dietary fat and obesity: evidence from epidemiology. Eur J Clin Nutr 49, 79–90. Macdiarmid JI, Cade JE, Blundell JE (1996). High and low fat consumers, their macronutrient intake and body mass index: further analysis of the National Diet and Nutrition Survey of British Adults. Eur J Clin Nutr 50, 505–512. Maeda N, Shimomura I, Kishida K, Nishizawa H, Matsuda M, Nagaretani H (2002). Diet-induced insulin resistance in mice lacking adiponectin/ACRP30. Nat Med 8, 731–737. Marques-Lopes I, Ansorena D, Astiasaran I, Forga L, Martinez JA (2001). Postprandial de novo lipogenesis and metabolic changes induced by a high-carbohydrate, low-fat meal in lean and overweight men. Am J Clin Nutr 73, 253–261. Marques-Lopes I, Forga L, Martinez JA (2003). Thermogenesis induced by a high-carbohydrate meal in fasted lean and overweight young men: insulin, body fat, and sympathetic nervous system involvement. Nutrition 19, 25–29. Marrades MP, Milagro FI, Martinez JA, Moreno-Aliaga MJ (2006). Differential expression of aquaporin 7 in adipose tissue of lean and obese high fat consumers. Biochem Biophys Res Commun 339, 785– 789. Marti A, Moreno-Aliaga MJ, Hebebrand J, Martinez JA (2004). Genes, lifestyles and obesity. Int J Obes Relat Metab Disord 28 (Suppl 3), S29–S36. Martinez JA (2000). Obesity in young Europeans: genetic and environmental influences. Eur J Clin Nutr 54 (Suppl 1), S56–S60. Martinez-Gonzalez MA, Martinez JA, Hu FB, Gibney MJ, Kearney J (1999). Physical inactivity, sedentary lifestyle and obesity in the European Union. Int J Obes Relat Metab Disord 23, 1192–1201. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC (1985). Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 53, 412–419. Moreno-Aliaga MJ, Santos JL, Marti A, Martinez JA (2005). Does weight loss prognosis depend on genetic make-up? Obes Rev 6, 155–168. Perez-Echarri N, Perez-Matute P, Martinez JA, Marti A, Moreno-Aliaga MJ (2005). Serum and gene expression levels of leptin and adiponectin in rats susceptible or resistant to diet induced obesity. J Physiol Biochem 61, 333–342. Pischon T, Girman CJ, Hotamisligil GS, Rifai N, Hu FB, Rimm EB (2004). Plasma adiponectin levels and risk of myocardial infarction in men. JAMA 291, 1730–1737. Prentice AM, Sonko BJ, Murgatroyd PR, Goldberg GR (1994). Obesity as an adaptation to a high-fat diet. Am J Clin Nutr 60, 640–642. Qi Y, Takahashi N, Hileman SM, Patel HR, Berg AH, Pajvani UB et al. (2004). Adiponectin acts in the brain to decrease body weight. Nat Med 10, 524–529. Raben A, Agerholm-Larsen L, Flint A, Holst JJ, Astrup A (2003). Meals with similar energy densities but rich in protein, fat, carbohydrate, or alcohol have different effects on energy expenditure and

European Journal of Clinical Nutrition


174 substrate metabolism but not on appetite and energy intake. Am J Clin Nutr 77, 91–100. Ravussin E (1995). Low resting metabolic rate as a risk factor for weight gain: role of the sympathetic nervous system. Int J Obes Relat Metab Disord 19 (Suppl 7), S8–S9. Ravussin E, Gautier JF (1999). Metabolic predictors of weight gain. IntJ Obes Relat Metab Disord 23 (Suppl 1), S37–S41. Reed GW, Hill JO (1996). Measuring the thermic effect of food. Am J Clin Nutr 63, 164–169. Sanchez-Villegas A, Delgado-Rodriguez M, Martinez-Gonzalez MA, De Irala-Estevez J, Seguimiento Universidad de Navarra group (2003). Gender, age, socio-demographic and lifestyle factors associated with major dietary patterns in the Spanish Project SUN (Seguimiento Universidad de Navarra). Eur J Clin Nutr 57, 285–292. Schrauwen P, van Marken Lichtenbelt WD, Westerterp KR (2000). Fat and carbohydrate balances during adaptation to a high-fat diet. Am J Clin Nutr 72, 1239–1241. Schutz Y (1995). Macronutrients and energy balance in obesity. Metabolism 44, 7–11. Schutz Y (2004). Concept of fat balance in human obesity revisited with particular reference to de novo lipogenesis. Int J Obes Relat Metab Disord 28 (Suppl 4), S3–S11. Schutz Y, Tremblay A, Weinsier RL, Nelson KM (1992). Role of fat oxidation in the long-term stabilization of body weight in obese women. Am J Clin Nutr 55, 670–674. Segal KR, Edano A, Blando L, Pi-Sunyer FX (1990a). Comparison of thermic effects of constant and relative caloric loads in lean and obese men. Am J Clin Nutr 51, 14–21.

European Journal of Clinical Nutrition

Segal KR, Edano A, Tomas MB (1990b). Thermic effect of a meal over 3 and 6 hours in lean and obese men. Metabolism 39, 985–992. Smith SR, de Jonge L, Zachwieja JJ, Roy H, Nguyen T, Rood JC et al. (2000). Fat and carbohydrate balances during adaptation to a high-fat. Am J Clin Nutr 71, 450–457. Stefan N, Bunt JC, Salbe AD, Funahashi T, Matsuzawa Y, Tataranni PA (2002). Plasma adiponectin concentrations in children: relationships with obesity and insulinemia. J Clin Endocrinol Metab 87, 4652–4656. Thomas CD, Peters JC, Reed GW, Abumrad NN, Sun M, Hill JO (1992). Nutrient balance and energy expenditure during ad libitum feeding of high-fat and high-carbohydrate diets in humans. Am J Clin Nutr 55, 934–942. Wadden TA, Butryn ML, Byrne KJ (2004). Efficacy of lifestyle modification for long-term weight control. Obes Res 12 (Suppl 15), S1–S62. Westerterp-Plantenga MS, Pasman WJ, Yedema MJ, WijckmansDuijsens NE (1996). Energy intake adaptation of food intake to extreme energy densities of food by obese and non-obese women. Eur J Clin Nutr 50, 401–407. World Health Organization (1985). Energy and protein requirements. Report of joint FAO/WHO/UNU expert consultation. In: WHO Technical Report Series No. 724. World Health Organization: Geneva, Switzerland. Zurlo F, Lillioja S, Esposito-Del Puente A, Nyomba BL, Raz I, Saad MF et al. (1990). Low ratio of fat to carbohydrate oxidation as predictor of weight gain: study of 24 h RQ. Am J Physiol 259, 6500–6507.