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Original Research Communications |
1 From the Laboratoire de Physiologie du Comportement Alimentaire, Ecole Pratique des Hautes Etudes, Bobigny, France, and the Laboratoire de Nutrition Humaine, Faculté Xavier Bichat, Paris.
2 Address reprint requests to D Chapelot, UFR Santé, Médecine et Biologie Humaine Léonard de Vinci, 74 Avenue Marcel Cachin 93017, Bobigny, France. E-mail: comp-alim{at}smbh.univ-paris13.fr.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Objective: This study investigated the role of leptin in the prandial pattern.
Design: In experiment 1, a spontaneous prandial pattern was recreated in 6 young, normal-weight men who were deprived of time cues and had blood withdrawn continuously at a frequency of one tube every 5 min. Meals were consumed ad libitum and dinner was requested voluntarily. Data from a second experiment, conducted in 8 subjects, were used to confirm the changes in leptin during the intermeal interval (IMI).
Results: Plasma leptin gradually rose to a peak (62 ± 18% of the lunch concentration) during the IMI and declined before the dinner request (-21 ± 4% of the peak concentration). This preprandial decline was confirmed in experiment 2 (-15 ± 9%). The leptin concentration at lunch and fat-free mass were the only significant predictors of the IMI (both: r2 = 0.91, P = 0.03). With fat intake at lunch, the leptin concentration at lunch was a positive predictor of the area under the curve of plasma fatty acids during the IMI (r2 = 0.95, P = 0.01). Moreover, the leptin concentration at lunch was negatively correlated with energy intake in the first course of this meal (r = -0.95, P < 0.005). A similar result was found at dinner (r = -0.85, P < 0.05). Last, the change in leptin was predicted accurately by changes in glucose, triacylglycerol, and fatty acids (r2 = 0.87, P < 10–5).
Conclusion: Plasma leptin concentrations increase during a spontaneous IMI and decline before the onset of a meal. The results argue for a role of leptin in the prandial pattern through fatty acid peripheral disposal.
Key Words: Prandial pattern • satiety • leptin • fatty acids • glucose • insulin • intermeal interval • men
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The modalities of leptin's action on food intake have yet to be established in detail. In humans, an inverse correlation between plasma leptin concentration and total energy intake in postmenopausal women (9) and between plasma leptin concentration and hunger-satiety ratings in obese women (10) was reported. Moreover, troglitazone, which improves insulin sensibility, yields opposite effects on leptin and hunger (11). However, it must be remembered that the prandial pattern hinges on 2 main factors: 1) satiety, the absence of ingestive motivation, which ends when the next meal is initiated, and, 2) satiation, the signal that interrupts the meal. The satiety produced by a meal is thus most accurately assessed by measuring the interval until the spontaneous request of the next meal; satiation is most accurately assessed by measuring the energy intake from the meal (ie, meal size). The absence of any change in blood leptin concentrations during a meal (12) and after isoenergetic meals that differ in composition (13) or in palatability (14) has been proposed as an argument against an effect of leptin on satiation. However, in rats, leptin administration was shown to reduce meal size but not the spontaneous intermeal interval (IMI) (15–17), suggesting that leptin affects satiation but not satiety. It was also shown that pretreatment with cholecystokinin increases the afferent gastric vagal sensitivity to leptin (18), favoring a role for leptin in the amount of food eaten during a meal. The inverse relation between plasma leptin and preprandial food-induced salivation (12) argues for a direct effect of leptin on the amount of food eaten because increased salivation was found to be positively associated with hunger and to reflect energy depletion (19). The recent finding that leptin inhibits LH neurons (20)—long known to be strongly involved in meal pattern (21)—also supports the short-term effect of leptin on meal pattern.
The aim of this study was to investigate the role of leptin in the prandial pattern of normal-weight subjects. Our purpose was to establish the relations between plasma leptin and 1) other plasma factors involved in energy substrate supply, and 2) spontaneous feeding variables (meal onset and the amount of food consumed). We used an experimental paradigm in which we recreated a spontaneous meal pattern in humans and monitored blood variables every 5 min.
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SUBJECTS AND METHODS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The experiment was completed by 6 lean, healthy men (Table 1) with a mean (±SD) age of 23.2 ± 2 y and a body mass index (BMI; in kg/m2) of 21.7 ± 1.8. Body composition was assessed by measuring the subcutaneous skinfold thickness. Fat-free mass (FFM) and fat mass (FM) were estimated to be 61.1 ± 6.1 and 9.0 ± 2.4 kg, respectively. The subjects gave written, informed consent before the experiment and were financially compensated for completing the study.
Design
The session was designed to determine in each subject deprived of time cues 1) the temporal pattern of plasma insulin, glucose, triacylglycerol, fatty acid, and leptin concentrations from before an ad libitum lunch until the spontaneous dinner request; 2) the amount of food eaten during the lunch; 3) the temporal pattern of hunger sensations from before the lunch until the dinner request; 4) the latency of the dinner request; and 5) the amount of food eaten during the ad libitum dinner.
Blood sampling and plasma assays
With the use of a specially designed double-lumen catheter (MTB, Amstetten, Germany) inserted into the antecubital vein, heparin-treated blood was continuously withdrawn over the whole session at a flow rate of 1.5 mL/5 min via a peristaltic pump without any back infusion of heparin into the vein (23). The heparin flow was 7000 U/L blood. Blood samples were collected via a tube-collection machine programmed to change tubes every 5 min. It took 10 min for blood to flow from the arm to the collection tube. Blood samples were then immediately centrifuged at 2000 x g for 15 min at 4°C, and plasma was transferred into 5 different tubes with a pipette and frozen to -30°C until assayed.
Plasma assays for leptin, glucose, insulin, fatty acids, and triacylglycerol followed standard procedures. Plasma leptin was determined by radioimmunoassay with a commercial Sensitive Human Leptin kit (Linco Research, St Charles, MO), which has a detection limit of 0.05 µg/L; in contrast, the Human Leptin radioimmunoassay kit has a detection limit of 0.5 µg/L. Mean intra- and interassay CVs were 3.5% and 5.3%, respectively. Insulin was determined by radioimmunoassay with the SB-INSI-5 kit (7% accuracy; CEA, Gif-sur-Yvette, France) with a lower level of sensitivity of 2 U/L. Glucose was measured by the glucose oxidase enzymatic method in a Yellow Spring Instruments glucose analyzer (1% accuracy; Bioblock, Strasbourg, France). Fatty acids and triacylglycerols were measured by using a colorimetric enzymatic method with C Wako and N Wako kits, respectively (both with 5% accuracy; Oxoïd, Dardilly, France).
Foods
The lunch meal was a 2-course meal and consisted of spaghetti Bolognese (5.2 kJ/g; 56% of energy as carbohydrate, 18% as fat, and 26% as protein) and praline-flavored ice cream (6.2 kJ/g; 57% of energy as carbohydrate, 30% as fat, and 13% as protein). Dinner was a buffet-style meal in which the subjects were presented with a variety of foods (first course: turkey breast, ham, eggs, tabbouleh, green peas, carrots, bread, cream cheese, Comté cheese; second course: chocolate cream, fruit yogurt, chocolate cookies, dark chocolate, apples, and bananas). All items were served in large portions. Water was provided ad libitum.
Hunger ratings
Subjective satiety feelings were assessed by using 100-mm visual analogue scales (VAS) preceded by the question "Do you feel hungry?" anchored by "not at all" and "extremely" at the left and right ends, respectively. The distance from the extreme left to the subject's vertical dash represented the rating score, expressed in mm. These scales were rated every 30 min but some scales were aleatory intercalated to prevent them from providing any time cue to subjects.
Procedure
The subjects were asked to eat their usual breakfast on the morning of the experimental day. When the subjects arrived at the laboratory at 1215, they were seated in comfortable armchairs and deprived of time cues by exposing them to artificial light and by removing all sources of visual and auditory time cues. The subjects were isolated under conditions that were quiet and comfortable enough in which to pursue their studies. At 1230, the indwelling catheter was inserted into an antecubital vein of the forearm and saline was infused for 30 min. Blood withdrawal started at 1300 and continued uninterrupted throughout the experiment. Lunch was served 30 min after the first blood sample was taken. The subjects were told to eat as much or as little as they wanted; the meal duration was not fixed, to encourage an ad libitum intake. When a subject spontaneously requested his next meal, he was asked to guess the current time. At this time, the catheter was withdrawn and the subject obtained his dinner from a buffet, served in a quiet room. A microwave oven was provided to heat the selected items. Before and after consumption, foods were accurately and covertly weighed to determine actual intakes.
Calculations and statistical analyses
Leptin pulsatility was determined by using CLUSTER (24), a computerized pulse-analysis algorithm. Parameters were 1 point for nadir and 2 points for test peak, with t statistics of 2.0 for up and down strokes. The fixed CV was the actual maximum CV of each subject's assay calculated from the replicated data available (3.4 ± 0.5%). The false-positive rate was estimated to be <5% (24). To allow statistical analyses within each subject, the 5-min plasma leptin concentrations were averaged in periods calculated on the basis of the time scales of our pulsatility data (one period = pulse + valley duration). Because we were interested mainly in the biological events preceding meal onset, the periods were calculated retrospectively from the blood sample corresponding to the dinner request back to the first available blood sample (ie, the 15–20-min interval before lunch). The pattern of change for glucose, insulin, triacylglycerols, and fatty acids was averaged on the same time basis. However, the pulsatilities of insulin and glucose were not analyzed because they were described previously (25) and because we focused our research on the relation between leptin and the prandial pattern.
For each blood index (leptin, insulin, glucose, triacylglycerols, and fatty acids), 3 values were determined: 1) a mean lunch concentration calculated as the mean value of the 4 measures preceding lunch and the first 2 prandial measurements, 2) a mean IMI value calculated as the mean of all the values observed from the end of the lunch meal until the request of the dinner meal, 3) an IMI area under the curve (AUC), determined by using the trapezoidal method. Given that the magnitude of glucose and fatty acid utilization by tissues is highly dependent on their respective absolute plasma concentration (26), the mean lunch AUC was not removed from the AUC. Last, leptin concentrations during the period preceding the dinner request, ie, the 6 preprandial blood samples, were used to determine the mean dinner leptin concentration.
To explore the relations between leptin and IMI and between leptin and insulin, glucose, triacylglycerols, and fatty acids, we conducted multiple regression analyses with the different biological (glucose, insulin, triacylglycerols, fatty acids, and leptin), anthropometric (BMI, FM, and FFM), and intake (amount of food, by weight or by energy) variables that could be expected to be predictive factors of the IMI; glucose, insulin, triacylglycerol, and fatty acid concentrations; and leptin AUCs. For each plasma variable, the mean IMI and the concentration at lunch were tested in turn.
The endocrine and metabolic model of the IMI was constructed by dividing the IMI of each subject into 20 intervals, each one representing 5% of the total IMI. Then, for each interval, a mean value of the measures included in this 5% interval was calculated for each variable and for each subject. To adjust for large interindividual differences in absolute values and to study plasma kinetics rather than plasma concentrations per se, blood variables were expressed as the percentage of the change from the baseline value according to the following equation:
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where yi is the value at each percentage interval, xi is the value of a measure featuring in the same percentage interval as yi, n is the number of measures in each interval, and b is the baseline concentration of leptin, ie, the first measured concentration after lunch.
The blood concentrations over each 30-min period were analyzed in each subject by univariate analysis of variance (ANOVA) for repeated measures with SYSTAT software (version 7.01; SPSS Inc, Chicago). Comparisons between leptin concentrations at each intermeal period and those at lunch and dinner were conducted by Scheffe's method. Correlations were calculated according to Pearson. The multivariate linear regression analyses were based on a backwards stepping procedure. The significance for including or rejecting a predictor was set at 0.15. To validate the equation, we completed each validation step according to the residuals and diagnostic (autocorrelation, Cook's distance) procedure.
Experiment 2
We had the opportunity to verify in a different group of subjects the preprandial decline in plasma leptin observed in experiment 1. The leptin data of these subjects were derived from the control condition of a study designed to pursue the investigation of previous works on the behavioral and metabolic consequence of snacks on metabolism (27, 28). Most of the experimental procedure was similar to that of experiment 1. Therefore, only methodologic differences with experiment 1 will be specified.
Subjects
This experiment involved 8 men (Table 2
) recruited through advertisements posted at Dijon Medical School, France. The mean age and BMI for the group were 22.6 ± 2.0 y and 22.5 ± 1.6, respectively. The protocol was approved by the Ethics Committee in Human Research at Dijon Hospital.
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Procedure
Blood withdrawal started 175 min after the beginning of the lunch meal and was interrupted 10 min after the request for the dinner meal.
Statistical analyses
Given that the duration of the measurements did not cover the whole IMI and that the leptin concentration at lunch and plasma triacylglycerol concentrations were not available, statistics were limited to the analysis of the predinner leptin profile in each subject by using repeated-measures ANOVA, with data averaged as in experiment 1. Significance was set at P < 0.05.
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RESULTS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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). Mean hunger just before the meal onset was significantly higher at dinner than at lunch.
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Leptin and prandial pattern
Results are illustrated on Figure 1
. On the basis of pulsatilities, the leptin data were analyzed within subjects on the basis of a 30-min interval beginning at the value observed at the time of the request for the dinner meal back to the first blood sample (15–20-min prelunch interval). The IMI was not a multiple of 30 in subjects 1 and 3. Therefore, in these subjects, the first period represented the mean of the 10–20-min and of the 0–20-min prelunch intervals, respectively.
First, we observed on the basis of the plasma leptin concentration at lunch that there was a significant postprandial increase in leptin concentrations in all subjects except subject 5. The increase became significant after 1–8 periods (ie, 30–240 min) across subjects. Compared with the plasma leptin concentration at lunch, the highest concentration during the subsequent IMI (peak concentration) represented a 62 ± 18% increase and was reached between the 10th and 13th periods (ie, 300 and 390 min).
We found that subjects 1, 2, 3, 5, and 6 requested their dinner meal when their leptin concentration had decreased significantly (each P < 0.05) compared with the peak concentration, with only a trend in subject 4 (NS). At the dinner request, the average leptin concentration (dinner concentration) was 21 ± 4% lower than the peak concentration. There was an interval of 1–3 periods (ie, 30–90 min) across subjects between the peak and dinner concentrations.
We calculated Pearson correlation coefficients between leptin concentrations at lunch and preprandial decreases in leptin to find out whether interindividual differences in leptin concentrations could have accounted for the between-subject variability in the preprandial decline in leptin concentrations. To take into account the between-subject differences in basal concentrations and also in intervals between peak and dinner concentrations, we calculated an AUC of the percentage of the preprandial decrease in leptin by subtracting the AUC of the observed leptin concentrations from the AUC if the leptin concentration had remained at the peak concentration (ie, at 100% of this peak concentration). We found that the leptin concentration at lunch was strongly correlated with the AUC of the preprandial percentage decrease in leptin (Figure 2A). Thus, the higher the leptin concentration at lunch, the greater was the decrease (calculated in AUC) in plasma leptin before the dinner request.
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Predictors of the IMI
The multiple regression analysis of the determinants of the IMI showed that the best-fitting model (Table 3) included only 2 predictors: the mean leptin concentration at lunch and the FFM (r2 = 0.91, P = 0.03). The tolerance between these 2 factors was high (0.99), suggesting a low intercorrelation. None of the other endocrine and metabolic factors reached the minimum 0.05 threshold and were excluded by the stepwise procedure:
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where L is the mean baseline plasma leptin concentration (in µg/L) at lunch, FFM is in kg, and IMI is the dependent variable (in min). The positive coefficients indicated that both a higher leptin concentration and a greater FFM correlated with a lengthier IMI across subjects. A Student's t test showed that predicted and observed values were not significantly different and the correlation coefficient between the predicted and observed values was high.
Predictors of glucose, insulin, triacylglycerols, fatty acids, and leptin AUCs
Glucose, insulin, and triacylglycerol AUCs could not be modeled with the predictors that we tested. However, it is of interest to note that a model was nearly significant for glucose AUC and for insulin AUC (P = 0.07 and 0.09, respectively). In each of these equations, mean leptin and insulin concentrations at lunch were the only factors involved. The best-fitting model for fatty acid AUCs also involved only 2 predictors: mean leptin concentration at lunch and dietary fat intake at this meal (r2 = 0.95, P = 0.01) (Table 3). All the other endocrine and metabolic factors were again excluded by the stepwise procedure:
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where F is the dietary fat intake (in kJ) derived from the lunch meal and fatty acid AUCs are during the IMI, ie, the dependent variable (in mmol•min/L). The positive coefficients showed that both a higher leptin concentration and a higher fat intake was correlated with higher fatty acid AUCs across subjects. A Student's t test showed that the predicted and observed values were not significantly different and the correlation coefficient between the predicted and observed values was high (Table 3). Leptin AUCs were accurately predicted by 3 factors: mean leptin, insulin, and glucose concentrations at lunch (r2 = 0.99, P < 10–4). This fitting was mainly due to the strong correlation between the mean leptin concentration at lunch and leptin AUCs (r = 0.96, P = 0.002).
We also found a high positive correlation (Figure 2B) between plasma leptin AUCs and plasma fatty acid AUCs. Conversely, leptin AUCs were not correlated with glucose, insulin, or triacylglycerol AUCs.
Leptin and energy intake
The leptin concentration at lunch was not significantly correlated with total energy intake at lunch. However, we found a strong negative correlation with the energy intake derived from the first course of the 2-course lunch (Figure 2C). Similar results were obtained between intake at the ad libitum dinner meal and predinner leptin concentrations, ie, between leptin and energy intake before the dessert items were consumed (Figure 2D). The leptin-energy intake ratios (µg•L-1•kJ-1) at lunch and dinner were highly correlated (r = 0.95, P < 0.005).
Determinants of the leptin variability
The percentage of variability relative to the immediate postprandial state of the 5 blood indexes is illustrated in Figure 3. The time units represent 5% of the individual spontaneous IMI. At 80% of the IMI, glucose and triacylglycerol concentrations fell below, whereas fatty acids rose above, their immediate postprandial concentration. Concomitantly, a decrease in leptin concentrations was observed, whereas insulin concentrations remained constant. Multivariate analysis showed that plasma glucose, triacylglycerol, and fatty acid concentrations were all significant predictors of leptin variability (r2 = 0.87, P < 10–5). The equation for leptin variability of each 5% interval of a spontaneous IMI is expressed as follows:
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where VL, VTG, VG, and VFA are leptin, triacylglycerol, glucose, and fatty acid variabilities, respectively. Pearson's correlation coefficients between the calculated VL and observed VL was high (r = 0.94, P < 10–7). Insulin variability (VI) was also a significant predictor of VL (P < 10–4), although its intercorrelation with glucose variability was high. Substitution of glucose with insulin did not improve the fitting of the equation.
Experiment 2
The spontaneous mean (±SEM) IMI was 432 ± 48 min and energy intake at dinner was 4932 ± 493 kJ. At the dinner request, the guessed time was not significantly different from the real time (-2 ± 23 min), but the range was large (-85 to 110 min; 94 ± 16 min) and in 6 of 8 subjects this difference was >30 min. The mean of the IMI for the group was 42.5 ± 4.6% when blood withdrawal began. ANOVAs showed significant variation in leptin during the IMI in each subject (all P < 0.01). Individual contrasts between 30-min periods confirmed that in 7 of the 8 subjects, the leptin concentration had decreased before the dinner request (Figure 4). As in experiment 1, this decline occurred with a widely varying delay between subjects, ie, beginning 1 to 4 periods (from 30 to 120 min) before the dinner request. The mean difference among subjects between the highest and the predinner leptin concentrations was 15 ± 9% (P < 0.02).
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. However, because leptin concentrations were not measured during the first part of the IMI, the variability was calculated relative to the mean leptin concentration of each subject during the available part of the IMI. Although the interindividual variation in the delay of leptin change was observed as in experiment 1, leptin variability increased significantly at 65% of the IMI (2.8 ± 2.0% compared with -6.1 ± 1.7% at 60% of the IMI; P = 0.001) and decreased significantly at 100% of the IMI (-0.2 ± 4.8% compared with 9.3 ± 3.0% at 75% of the IMI; P = 0.05). The variability was not significantly different between 100% of the IMI and each of the preincrease concentrations (ie, 50%, 55%, and 60% of the IMI).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Under these conditions, we confirmed in experiment 1 the results on leptin pulsatility reported by Licinio et al (31, 32) with a comparable, albeit slightly shorter, pulse duration (27.0 ± 2.2 and 32.8 ± 1.6 and 34.6 ± 2.0 min, respectively). This difference can probably be explained by the more frequent blood sampling in our study (every 5 compared with every 7 min). With a 10-min blood-sampling frequency, other researchers (33) reported much longer pulse durations (70.0 ± 4.3 min), suggesting that pulsatility may actually be shorter than that found by Licinio et al or the present study. More frequent blood sampling may allow clarification of this point.
As reported previously (32), we confirmed that there was a high positive association between the magnitude of the pulse concentration and plasma leptin concentration. However, we found a negative association between these 2 factors, although of lower significance, when pulse height was expressed as a percentage of the increase. Thus, the relative increase during a pulse seems to be inversely related to the initial concentration of leptin.
Plasma leptin concentrations increased after the subjects consumed an ad libitum lunch. This increase was not observed in the immediate postprandial period but occurred between 30 and 240 min later. This result is consistent with the observations of other authors (14, 34). However, in those studies, energy intake at the beginning of the test was fixed and no meal was provided nor was permitted during the following 8 h. Thus, it remained to be shown whether this increase in leptin concentration could occur within a normal IMI after an ad libitum meal. In the present study, the magnitude of the increase in leptin concentrations showed considerable interindividual variability. This may suggest that this plasma profile is not an obligatory phenomenon of the normal IMI pattern. That this increase in leptin concentration was concomitant with the rise in hunger feelings might be considered paradoxical because some authors reported recently an inverse correlation between leptin concentrations and hunger feelings (10, 11). However, in 5 of the 6 male subjects, plasma leptin concentrations actually decreased before hunger reached its maximal score and dinner was requested. We showed in experiment 2 that this result was reproducible because it was confirmed in 7 of 8 male subjects. In both experiments, this decline was observed with a markedly different delay between subjects and preceded the request for dinner with a variable interval. Note that a trend for a similar decrease was not significant in subject 4 in experiment 1 and in subject 3 in experiment 2 because the predinner decrease and the peak pulse, respectively, were too short (20 min) compared with the 30-min averaged periodicity. Whereas the decrease in leptin concentration is usually considered as the consequence of a fast (35), our results show that this occurs during a spontaneous IMI.
Although the sample was small in experiment 1, we surmised that the accuracy of our experimental procedure would enable us to determine the predictors of the prandial pattern using multiple linear regression analysis. The analysis actually showed that the leptin concentration at lunch was a strong positive predictor of the subsequent IMI. None of the other blood indexes (insulin, glucose, triacylglycerols, and fatty acids) were significant predictors of the IMI. The other factor involved in the predictive equation of the IMI was FFM, which was also a positive predictor of IMI. The fact that the duration of the IMI is the most accurate indication of satiety supports a role for leptin as a satiety factor.
However, the first question one might ask is, how could leptin modulate IMI? We showed previously that fatty acids contribute to the meal pattern by delaying the onset of the next meal (29). This is likely to be mediated by the glucose-sparing effect of fatty acids, which is known as the Randle glucose-fatty acid cycle (36). In the late part of the spontaneous IMI, low glucose and, therefore, low insulin concentrations permit the release and oxidation of fatty acids, as shown by an increase in plasma fatty acid concentrations (27, 29) and a decrease in the respiratory quotient (personal data). As hypothesized, leptin was actually the only biological predictor of the fatty acid AUCs during the IMI. The other factor was fat intake at lunch. On the basis of a series of recent studies, one hypothesis that has emerged is that leptin acts via an increase in the availability of fatty acids for oxidation during the IMI. For instance, leptin was shown to enhance plasma fatty acid concentrations (37) and fatty acid oxidation (38, 39). Furthermore, the increase in fatty acid concentrations inhibits glucose oxidation (40) and increases fatty acid oxidation (41). When the insulin concentration declines, the reduction in insulin-induced lipolysis inhibition enhances fatty acid disposal for oxidation. Our plasma insulin and fatty acid concentrations confirmed this endocrine and biological model of IMI. Moreover, the fall in leptin concentrations occurred when insulin had reached its preprandial concentration and when fatty acid concentrations had increased significantly. Thus, our results suggest that leptin is involved in eating behavior via the extent to which fatty acids are reintroduced in the oxidative pathway between 2 meals. Consistent with this, it was reported recently that the action of leptin on estrus was blocked when fatty acid or glucose oxidation was suppressed (42).
That leptin is a predictor of IMI may seem inconsistent with the results in rats that leptin reduced meal size but not IMI. However, we did find a high positive correlation between leptin concentrations at the beginning of each meal and intake in the first course of this meal, whereas there was not even a trend for a similar correlation with the second course (ie, dessert item) or when the dessert was included. Given that the meal could not be separated into 2 courses in experiment 2, it was not possible to verify this relation. It must be remembered that Booth et al (43) showed in humans that the adaptive behavior was more efficient for the first than for the last course of a 3-course meal. The involvement of leptin in this phenomenon will need to be assessed.
Note that we observed no correlation between leptin and any of the other blood indexes at the beginning of lunch. Furthermore, none of these blood indexes were significantly associated with intake or IMI, suggesting that the relation between leptin and energy intake was not mediated by these factors.
Finally, we proposed for the first time an endocrine and biological model of the IMI. In expressing individual leptin values as percentages of change from baseline and the temporal data as percentages of individual IMIs (in 5% increments), we obtained a representation of the endocrine and substrate variability during an IMI preceded and followed by an ad libitum meal. This model was confirmed in experiment 2 for the second part of the IMI and for leptin. It is striking that on the basis of this mathematical construction of the IMI, we found in experiment 1 that the variability of leptin per unit of IMI can be accurately predicted by the 3 substrates required to deposit triacylglycerols in adipocytes. Each of these substrates was a negative predictor of leptin variability, suggesting that the withdrawal of glucose and triacylglycerols from the circulation by adipocyte uptake contributes to increase leptin secretion, whereas release of fatty acids in the circulation from adipocytes contributes to a drop in leptin secretion. Insulin was also a strong predictor of leptin variability when glucose was removed from the equation, but did not improve the fitting. In line with this, van Aggel-Leijssen et al (44) reported recently that variations in leptin during a complete diurnal cycle were highly correlated with fatty acid and glucose variations. This may explain why the diurnal rhythm of plasma leptin concentrations is more meal-entrained than endogenous (45). This also suggests that there is no short-term effect of insulin on leptin when the substrate disposal for the triacylglycerol adipose depot is low. From the whole of these arguments, it seems that leptin is sensitive not only to extreme feeding conditions (from fasting to overconsumption), but also to the normal variations in substrates observed during interprandial metabolism.
In conclusion, we found that after an initial increase, plasma leptin concentrations decline before the hunger-triggered onset of a meal. This variation in leptin is unlikely to be the metabolic signal of intake, but rather is a sign of the enhanced plasma fatty acid disposal in the late IMI. We also present arguments for a role of leptin in short-term food intake. The plasma leptin concentration at lunch was a strong predictor of the subsequent duration of the IMI across subjects and was a strong predictor of subsequent plasma fatty acid AUCs. These results favor a direct influence of leptin on satiety and suggest that this action is mediated through fatty acid disposal for oxidation. Furthermore, we observed that the leptin concentration at the beginning of a 2-course meal was strongly correlated with energy intake in the first course of this meal, either at lunch or at dinner. These results provide arguments for the first time in humans that leptin is involved in the spontaneous prandial pattern and therefore in the short-term control of food intake. Last, we showed that an endocrine and metabolic pattern of IMI can be represented with the inclusion of leptin. Further studies are needed to define in more detail the interrelation between endocrine and behavioral indexes and to determine how certain patterns of eating behavior may impair the homeostatic function of leptin.
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ACKNOWLEDGMENTS |
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REFERENCES |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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) and corresponding hunger ratings on visual analogue scales (•); 
indicates when the lunch meal was fed. Right column: mean (±SEM) plasma leptin concentrations averaged over 30-min periods during the lunch period (black bar), at the time of the request for the next meal (dinner period; hatched bar), and at the time between these 2 periods (white bars). After an overall ANOVA (P < 10–3 in all subjects), contrasts were conducted by Scheffe's method: asignificantly different from the black bar, P < 0.05; bsignificantly different from the hatched bar, P < 0.05. Note that because of the large interindividual variations in concentrations, the y axes vary between the 6 subjects.
