Energy nutrients modulate the splanchnic sequestration of
dietary nitrogen in humans: a compartmental analysis
H.
Fouillet,
C.
Gaudichon,
F.
Mariotti,
C.
Bos,
J. F.
Huneau, and
D.
Tomé
Unité Mixte de Recherche de Physiologie de la Nutrition et du
Comportement Alimentaire, Institut National de la Recherche
Agronomique-Institut National Agronomique Paris-Grignon, F-75231
Paris, France
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ABSTRACT |
We used a
previously developed compartmental model to assess the postprandial
distribution and metabolism of dietary nitrogen (N) in the splanchnic
and peripheral areas after the ingestion of a single meal containing
milk protein either alone (MP) or with additional sucrose (SMP) or fat
(FMP). The addition of fat was predicted to enhance splanchnic dietary
N anabolism only transiently, without significantly affecting the
global kinetics of splanchnic retention and peripheral uptake. In
contrast, the addition of sucrose, which induced hyperinsulinemia, was
predicted to enhance dietary N retention and anabolism in the
splanchnic bed, thus leading to reduced peripheral dietary amino acid
availability and anabolism. The incorporation of dietary N into
splanchnic proteins was thus predicted to reach 18, 24, and 35% of
ingested N 8 h after MP, FMP, and SMP, respectively. Such a model
provides insight into the dynamics of the system in the nonsteady
postprandial state and constitutes a useful, explanatory tool to
determine the region-specific utilization of dietary N under different
nutritional conditions.
protein metabolism; postprandial period; insulin; mathematical
model; parameter estimation
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INTRODUCTION |
CONSIDERABLE
ATTENTION has been paid to interactions between energy nutrients
and nitrogen (N) metabolism, leading to the conclusion that
carbohydrate (CHO) is more likely than fat to exert a sparing effect on
body protein (25, 30, 33, 42). Certain studies have
addressed the effects of dietary CHO (33, 48) or insulin
availability (32) on splanchnic N anabolism in the fed
state, whereas others have aimed at studying the effects of amino acids
(AA), CHO, and/or insulin on the stimulation of muscle protein
synthesis (5, 11, 41, 44). However, little is still known,
particularly in humans, about the dietary N partitioning between
splanchnic and peripheral tissues and its immediate orientation in the
anabolic and catabolic pathways of different organs after protein
ingestion. The acute response to a meal is known to involve a cascade
of transient metabolic processes, where the rate of dietary AA and
energy nutrient absorption and the resulting pattern of their
postprandial concentrations in tissues are potent modulators of protein
synthesis, breakdown, and oxidation (8). The balance between dietary AA anabolic and catabolic pathways depends on the
tissue under consideration and varies as a function of dietary factors,
especially protein or energy intake (2, 49). Liver and
muscle, the most important tissues involved in postprandial protein
turnover, do not participate in these metabolic processes in the same
way because of their different specificity (high rate of protein
turnover in the liver vs. large mass of the muscle protein pool)
(33).
The aim of the present study was to further investigate
whether fat and/or sucrose differentially affects the postprandial partitioning of dietary N between splanchnic and peripheral organs and
its regional metabolic fate under the dependence of AA absorption kinetics and insulin response. For this purpose, we used a
compartmental modeling tool that we had previously developed to
specifically follow ingested N in the fed state and determine its
dynamic fate through free and protein-bound AA from the splanchnic and
peripheral organs in humans (24). It consists of an
11-compartment model, the behavior of which has been validated using
experimental measurements of 15N kinetics in ileum, blood,
and urine after the ingestion of a single 15N-labeled
dietary protein meal (MP) in humans. This modeling approach mimics the
absorption and physiological fate of dietary N in the postprandial
nonsteady state, whereas no such information could usually be obtained
with regard to the kinetic aspects of AA disposal or release during
classical steady-state studies of N metabolism (7, 26, 35, 36,
45, 50). Furthermore, the model allows the simulation of both
regional dietary N distribution in the postprandial phase and the
relative ability of a protein meal to promote protein synthesis in
either the splanchnic or peripheral areas. In contrast, organ balance
studies (23, 32, 38, 43) allowed determination of
only the net organ balance of AA, without discriminating between
dietary and endogenous AA metabolism (7, 35), whereas the
multiple-tracer approach (6, 7, 27) addressed the
splanchnic extraction of dietary AA without distinguishing that part
used for protein synthesis from the remaining deaminated part of the
splanchnic uptake (7).
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MATERIALS AND METHODS |
Collection of experimental data.
Experimental data were collected as described previously
(25). The protocol was approved by the Institutional
Review Board for Saint-Germain-en-Laye Hospital, and informed consent
was obtained from each subject. After an overnight fast, healthy humans
equipped with an ileal tube ingested a liquid meal made up of 30 g
of 15N-labeled milk protein either alone (MP group,
n = 8), or with additional energy in the form of either
100 g of sucrose (SMP group, n = 9) or 43 g
of milk fat (FMP group, n = 9). The energy content of
the diets ranged from 500 kJ for MP to ~2,150 kJ for SMP and FMP.
Over a period of 8 h after the meal, samples of intestinal effluents were collected on a continuous basis, with blood samples being drawn hourly and urine collected under mineral oil every 2 h. Analytical methods and calculations have been described in full
detail elsewhere (25). Briefly, urinary urea and ammonia, as well as plasma urea and free AA, were separated using cation exchange resins. Urea and ammonia in urine and plasma were determined on clinical analyzers and N in ileal samples by use of an elemental analyzer. Plasma insulin was measured by a radioimmunoassay
method. 15N enrichments in ileal effluents, plasma
free AA and urea, and urinary urea and ammonia were determined by
isotope ratio mass spectrometry. Dietary N in the samples was then
determined using isotope dilution equations (for a full description,
see Ref. 25). Ileal flow rates were assessed using a
slow-marker technique (28). Body urea was calculated from
plasma urea by the consideration that urea mixes uniformly in total
body water, the total body water value being determined using the
equations developed by Watson et al. (47). The amount of
dietary N present in plasma free AA was calculated by assuming that the
plasma AA concentration was 100 mg/l and the mean plasma volume was 5%
of the body mass (24). All data (dietary N recovered from
cumulated ileal effluents, plasma free AA, body urea, cumulated urinary
urea, and cumulated urinary ammonia) were converted into percentages of
ingested N. Urinary data were interpolated, and ileal effluent data
were pooled in such a way as to obtain the same 1-h data step size.
Linear compartmental model.
The linear compartmental model was previously selected using SIMUSOLV
software and was validated at each stage of its development by testing
successively its a priori (theoretical) and a posteriori (numerical)
identifiability (24). This model consisted of 11 compartments representing distinct amounts of dietary N and 15 different pathways of exchange between these compartments, each being
characterized by a constant diffusion coefficient,
ki,j (Fig. 1).
These exchange rate constants, ki,j, represented
the fraction of dietary N in compartment j transferred to compartment i
per unit of time (min). The model, developed using average data values
for the MP meal, described the transfer of dietary N through the
gastrointestinal tract, the elimination of absorbed dietary N in the
urine, and the distribution of retained dietary N in the body. The
model structure included three subsystems to cover all experimental
data: the gastrointestinal tract subsystem built using ileal effluent
data, the deamination subsystem covering body urea, urinary urea and
urinary ammonia data, and the retention subsystem built using plasma
free AA data. The retention subsystem was structured with the aim of
clarifying N distribution between splanchnic and peripheral tissues,
and this selected structure distinguished between free and
protein-bound AA in both areas. Parsimonious modeling was applied to
the choice of each subsystem structure so that it would be the minimum
necessary to fit the sampled compartments.

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Fig. 1.
The selected model. Circles indicate compartments
representing kinetically distinct pools of dietary nitrogen (N); arrows
between the compartments represent transfer pathways; and numbers by
the arrows indicate transfer rate constants
(ki,j). Bullets indicate the compartments
sampled. Samples s1-s5 represent cumulative ileal effluents, plasma
free amino acids, body urea, and cumulative urinary urea and ammonia,
respectively, and are associated with compartments 3, 5, 9, 10 and 11. A unidirectional chain of 3 compartments was
used to describe the gastrointestinal tract: bolus input is assumed to
take place in compartment 1, representing the gastric N
content; compartment 2 corresponds to the intestinal lumen N
content and compartment 3 to entry into the cecum (ileal
effluents) from which fecal losses take place. Compartment
4, belonging to the retention subsystem, corresponds to splanchnic
free AA exchanging bidirectionally with the intestine (absorption and
release into the intestinal lumen). Two irreversible losses occur from
compartment 4, one through the body urea (compartment
9), from which urinary urea is irreversibly lost
(compartment 10), and the other representing urinary ammonia
losses (compartment 11), these last 3 compartments composing
the deamination subsystem. Compartment 4 (SA) exhibits more
bidirectional exchanges with 2 other compartments of the retention
subsystem, 5 and 7. Compartment 7 corresponds to the splanchnic protein pool, and reversible pathways
between 4 and 7 traduce the synthesis and
degradation phenomena that occur in the splanchnic bed.
Compartment 5 represents plasma free AA, exhibiting more
bidirectional exchanges in a catenary structure with compartments
6 and 8 of the retention subsystem, representing
peripheral free AA and peripheral protein, respectively.
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Parameter estimation.
This compartmental model was confronted with the experimental data
obtained after the ingestion of MP, SMP, and FMP meals. SIMUSOLV
software was used to estimate those parameter values that would produce
the closest model predictions for each meal by adjusting the rate
constant values until the model predictions fitted the data for all
sampled compartments simultaneously. The objective function iteratively
maximized during the parameter estimation process under SIMUSOLV was
the log of the likelihood function, which represents the joint
probability of obtaining our experimental data for each sampled pool in
the context of a given set of fitted parameters, taking account of the
fact that an experimental error is always associated with experimental
measurements (20, 24). First, we explored a broad range of
variations for all parameters, allowing values to change individually
or in various combinations to fit each meal. The correlation matrix
provided from statistical output showed that the two parameters of
certain bidirectional pathways were always strongly correlated. We
therefore decided to keep one of these parameters for each pair
(k2,4, k4,5, k5,6, and k6,8) constant
and equal to a value determined during the previous step of parameter
estimation, changes to other parameter values being both necessary and
sufficient to explain the observed differences in kinetics. In this
way, by making minor adjustments to the smallest set of parameters, we
took advantage of Berman's minimal change postulate (4).
In the context of our study, this implies that we could characterize
the changes ensuing from our experimental perturbation (presence and
type of additional energy content in the meal) by exploring the minimal
change to the "reference" model (MP meal), thus bringing the new
predictions into line with the new data. These parameters, in which
changes were identified as contributing most to the difference
in the meals, were considered as regulatory steps involved in the
metabolic response to variations in meal composition. Moreover,
different values for initial parameter estimates were tested to reduce
the probability of falling into a local optimum if the starting point was not in the neighborhood of the global optimum. Final parameter estimates were verified as providing the best possible fit and not a
local optimum. Thus, for each meal, the model was quantified for each
subject and for the mean of individual data by use of parameter
estimation. Results concerning dietary N postprandial distribution
after each meal were obtained by optimization with the use of the mean
of individual data for each meal, whereas individual fits were used to
assess the discriminatory capacity of the model and the statistical
differences between meals.
Sensitivity analysis.
Sensitivity analysis of the model was performed by evaluating the
effect of a 1% change in parameter value on the prediction of a
variable response, i.e., by calculating a sensitivity coefficient for
each pair:
(model response)/
(model parameters). However, to
eliminate the bias caused by the magnitude in parameter values, the
sensitivity coefficients were log normalized and calculated using the
direct decoupled method under SIMUSOLV (20). Sensitivity analysis was performed on the parameter estimate values obtained for
each meal by evaluating their influence on the model responses for each
compartment and also for the retention subsystem.
Statistical analysis and other calculations.
The discriminatory capacity of the model was tested by discriminant
analysis by means of the SYSTAT statistical package (SYSTAT, Evanston,
IL), and predicted differences in the distribution kinetics between
dietary groups (i.e., meals) were analyzed by repeated-measures ANOVA
using a general linear model procedure (SAS/STAT 6.03, SAS Institute,
Cary, NC). Differences with P values <0.05 were considered statistically significant.
Peripheral protein synthesis efficiency, defined as the fraction of the
intracellular AA rate of appearance that is incorporated into
protein, was calculated as the flux of dietary N incorporation into the
peripheral protein compartment (PP) divided by the flux of dietary N
appearance in the peripheral free AA compartment (PA)
(45), i.e.,
(k8,6 · PA)/[(k6,5 · AA)+(k6,8 · PP)].
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RESULTS |
Parameter estimation and numerical validation.
For each meal, the model was quantified for each subject and for the
mean of individual data by use of parameter estimation. The model
fitted all subjects satisfactorily, but a better fit was obtained for
each compartment with the use of the mean of individual data (Fig.
2). The corresponding optimization
criteria and parameter estimate values for each meal are given in
Tables 1 and
2. The distribution of parameter
estimates did not differ significantly when obtained using the mean of
individually fitted parameters or when directly fitting the mean of
individual data (Wilcoxon matched-pair signed-ranks test, two-tailed
P
0.95).

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Fig. 2.
Fits obtained after optimization with use of the mean data for each
meal: observed vs. predicted values for each sampled compartment. Each
observed datum is plotted by value ± 2 SD, the weighting scheme
being determined by SIMUSOLV software during optimization. A, B,
C, D, and E: cumulative ileal effluents, kinetics of
body urea, kinetics of plasma free AA, and cumulative urinary urea and
urinary ammonia, respectively, after milk protein (MP), MP + milk
fat (FMP), and MP + sucrose (SMP) meals. Lines, computer
simulations: bold, after MP; solid, after FMP; dotted, after SMP.
Points, experimental data: , after MP;
, after FMP; , after SMP.
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Table 1.
LLF values and percentage variations explained after each meal and
detailed for each sampled compartment
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For each meal, the numerical identifiability of the model was tested
successively by checking the goodness of fit, the randomness of
residual errors, and the reliability of parameter estimates (15). Goodness of fit, which appeared very acceptable from
a visual inspection of a plot of model predictions vs. experimental data (Fig. 2), was further assessed by an analysis of residuals to
check the underlying assumptions of both the normality and the
randomness of the data error distribution involved in optimization (24). If the assumption of normality is valid,
standardized residuals, i.e., the difference between a datum and its
model prediction divided by the standard deviation of the datum, should follow a normal distribution, with a mean of 0 and a variance of 1. Thus 95% of standardized residuals should lie within the range
1.96
to +1.96 (24). For each meal, the standardized residuals of sampled compartments were within or close to the 95% interval range
(93% for MP and 98% for FMP and SMP). To ensure that datum points
were scattered randomly around the fitted curve, nonrandomness in the
residuals was tested formally using the runs test, which counts the
number of consecutive residuals with the same sign and compares it with
the number of runs expected if the residuals were randomly scattered
(24). The residuals of each sampled compartment were
generally consistent with the hypothesis of randomness [P value of runs tests >0.75 (15)], except
for UA in the FMP group and E in the SMP group (P < 0.15). These systematic but slight deviations between experimental data
and model predictions for UA in the FMP group and E in the SMP group
suggested that the model is too simple to fit these data sets
accurately under these particular nutritional conditions and may
require more compartments than those postulated (14, 15).
For example, delayed intestinal transit in the presence of sucrose
(after SMP) may require at least one more compartment to fit the ileal
effluent data more adequately. However, we were satisfied with the
model response, even in these two less adequately fitted compartments,
because these two sampled compartments constitute traps from which
there is no return of dietary N to any compartment outside the trap; this lack of structural influence on the remainder of the system limits
the consequence of the committed error. The precision of fitted
parameters is commonly expressed in terms of a coefficient of variation
(CV), calculated as the ratio of the standard deviation of a parameter
to its value, parameter values with a CV <50% usually being
judged as adequately estimated (24). As shown in Table 2,
the highest CV for fitted parameters was lower than 8, 17, and 40% for
MP, FMP, and SMP meals, respectively, indicating that the parameters
were estimated with very good precision.
Discriminatory capacity of the model and sensitivity analysis.
The discriminatory capacity of the model was tested using a
discriminant analysis that optimally separates all individual sets of
ki,j into groups of closer characteristics on
the basis of linear combinations of the parameters. The discriminatory
capacity of the model was assessed by determining the extent to which
subjects who received the same meal were a posteriori correctly grouped together. Whatever the comparison (i.e., when comparing two meals: MP
vs. SMP, MP vs. FMP, SMP vs. FMP, or the three meals together: MP vs.
FMP vs. SMP), 100% of subjects were correctly replaced in their
original group. The discriminant analysis further indicated that
k2,1, the gastric emptying rate,
k7,4, the splanchnic protein incorporation rate,
and k6,5, the peripheral tissue transfer rate, accounted for most of the differences between/among meals.
As previously reported for the pure MP meal (24), the
results of the sensitivity analysis, which enabled identification of
those parameters with the greatest influence on the system, agreed with
the model structure and our knowledge of system behavior. Whatever the
compartment and subsystem, k2,1 and
k4,2 exhibited considerable initial influence,
which then declined more slowly after FMP than after MP and even more
slowly after SMP than after FMP. After all three meals, the retention
subsystem was rapidly positively sensitive to
k5,4, whereas it was negatively influenced by
variations in k9,4 and
k3,2 in descending order, these trends increasing over time (results not shown). Furthermore, the positive influence of k7,4 and
k2,1 on the retention subsystem was further predicted for SMP. Figure 3 shows the
relative influence of fitted parameters on the regional protein
compartments (SP and PP). As shown in Fig. 3, A and
B, after both MP and FMP, SP was most positively sensitive
to variations in k7,4 and negatively sensitive
to k4,7 and, to a lesser extent, to variations
in k5,4 and k9,4. After SMP, both a
greater positive influence of k2,1 and a lower
negative influence of k4,7 were predicted on SP
(Fig. 3C). Moreover, after MP and FMP, PP was positively
sensitive to variations in k5,4 and
k8,6, in descending order, and negatively to
those in k7,4 and k9,4, these latter trends
increasing over time (Fig. 3, D and E). After
SMP, both a stronger positive influence of k2,1 and a stronger negative influence of k7,4 were
predicted on PP (Fig. 3F). To summarize,
k2,1 and k4,2 on the one
hand, and k5,4, k9,4, and
k7,4 on the other hand were systematically
identified as important governing parameters, as previously reported
for the MP meal (24). Nevertheless, for the SMP meal, the
gastric emptying (k2,1) and splanchnic protein
synthesis (k7,4) rates exerted greater influence
on the metabolic fate of dietary N.

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Fig. 3.
Relative sensitivity of fitted parameters
(k2,1, k3,2,
k4,2, k4,7,
k5,4, k6,5,
k7,4, k8,6,
k9,4, k10,9,
k11,4) to dietary N incorporation into the
splanchnic protein compartment (SP) after optimization with use of data
obtained with MP (A), FMP (B), and SMP
(C), and into the peripheral protein compartment (PP) after
optimization using data on MP (D), FMP (E), and
SMP (F).
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Predicted kinetics of dietary N absorption and transfer to
splanchnic and peripheral organs.
The model enabled simulation of the successive transfers of dietary N
between different compartments after each meal. By achieving a very
good fit with the data for each meal, the model reported, as
experimentally measured, the same digestibility of ~95% of dietary
N at 8 h whatever the meal, with global deamination (= BU + UU + UA) that was similar after MP and FMP meals (any
statistical difference at each point and regarding the global kinetics)
but significantly lowered after SMP, which was the only meal that was
seen to induce a significant postmeal insulinemic response (Fig.
4) (25). The model simulated
an N gastric emptying half-time that rose from ~20 min after MP to
~50 min after FMP and to 100 min after SMP (Fig.
5).

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Fig. 4.
Incremental plasma insulin responses to MP
( , n = 8), FMP ( , n
= 9) and SMP ( , n = 9) meals. Values
are means ± SE; significantly different from the basal value
(t-test, P < 0.05). Differences between
meals were tested using a general linear model (GLM) procedure for
repeated measures: P < 0.05 SMP vs.
MP; *P < 0.05 SMP vs. FMP.
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Fig. 5.
Model predictions for the kinetics of dietary N in the
gastric compartment (G), in %ingested N over time after each meal.
Lines, model predictions: bold, after MP; solid, after FMP; dotted,
after SMP. Values are obtained by optimization with use of the mean of
individual data. Differences between meals were tested using a GLM
procedure for repeated measures: P < 0.05 MP vs. FMP; P < 0.05 MP vs. SMP;
*P < 0.05 FMP vs. SMP.
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Unlike experimental data, the simulation enabled evaluation of the
partitioning of retained dietary N between and within the splanchnic
and peripheral areas. The retention of dietary N in the splanchnic bed
(= SA + SP) occurred rapidly and transiently after MP, with a high
and acute peak at ~1 h after the meal, whereas FMP, and particularly
SMP, induced slower, lower, and more prolonged splanchnic retention
with a peak achieved at ~2.5 and ~6 h after FMP and SMP,
respectively (Fig.
6A).
Despite delayed and transiently higher retention between 4 and 8 h, the splanchnic retention pattern after FMP was closer to that of MP,
leading to a global kinetic of dietary N splanchnic retention that was
not significantly affected by the addition of fat. In contrast,
splanchnic retention after SMP was found to be significantly higher
than after MP as early as 5 h and in terms of global kinetics.
These differences in splanchnic retention were due mainly to the
pattern of dietary N incorporation into splanchnic protein, which fell
after ~5 and ~4 h with MP and FMP, respectively, with corresponding
maximum values for ingested N of 21 and 32%, respectively, whereas the
level was still rising after 12 h with SMP, reaching a maximum
value of 37% of ingested N at this time (Fig. 6C). Like the
retention, the transfer of dietary N to the splanchnic protein
compartment (SP) in the presence of sucrose was significantly higher
after 5 h and for the global kinetic when compared with MP,
whereas fat significantly increased splanchnic dietary N incorporation
but only during the first 7 h. Concurrently, the transient
incorporation of dietary N to the splanchnic free AA compartment (SA)
decreased after ~1, ~1.5, and ~2 h after MP, FMP, and SMP,
respectively, with corresponding maximum values for ingested N of 47, 27, and 22%, respectively (Fig. 6B). These delaying and
lowering effects of sucrose, and to a lesser extent of fat, on dietary
N accumulation in the SA compartment were significant in both cases,
but only during the first 2 or 3 h after the meal.

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Fig. 6.
Model predictions for the kinetics of the
splanchnic retention of dietary N (A) and its partition
between free AA (B) and proteins (C) after each
meal. Lines, model predictions: bold, after MP; solid, after FMP;
dotted, after SMP. Values are obtained by optimization with use of the
mean of individual data. Differences between meals were tested using a
GLM procedure for repeated measures: P < 0.05 MP vs. FMP; P < 0.05 MP vs.
SMP; *P < 0.05 FMP vs. SMP.
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Splanchnic retention and anabolism, facilitated during the earlier
phase of the feeding period, were followed by a redistribution of
dietary N from these to peripheral tissues during the later feeding
period and on into the postabsorptive phase. The peripheral uptake of
dietary N (= PA + PP) was thus still increasing 12 h after
the three meals, with maximum peripheral retention values achieved over
the simulation period of 51, 46.5, and 38% of ingested N for MP, FMP,
and SMP, respectively (Fig.
7A). The
lowering effect of sucrose on dietary N transfer to peripheral tissues
was found to be significant at each time point and for the global
kinetic, whereas it was significant during only the first 8 h in
the presence of fat. Similarly, incorporation into the peripheral
protein compartment (PP) was still increasing 12 h after the three
meals, reaching maximum values at this time of 43, 37.5, and 33% of
ingested N in the MP, FMP, and SMP groups, respectively (Fig.
7C). This lowering effect was found to be significant at
each time point and for the global kinetic in the presence of sucrose,
but not of fat. The dietary N content in the peripheral free AA
compartment (PA) decreased after ~3.5, ~4, and ~5 h after MP,
FMP, and SMP, respectively, with corresponding maximum ingested N
values of 21.5, 16, and 12.5%, respectively (Fig. 7B). This
delaying and lowering effect on PA was found to be significant only
during the first 5 h after the meal containing fat, whereas it was
significant at each time point and for the global kinetic in the
presence of sucrose. Moreover, the peripheral protein synthesis
efficiency (PSE) significantly increased from ~25% after both MP and
FMP to 31% after SMP ingestion.

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Fig. 7.
Model predictions for the kinetics of the
peripheral retention of dietary N (A) and its partition
between free AA (B) and proteins (C) after each
meal. Lines, model predictions: bold, after MP; solid, after FMP;
dotted, after SMP. Values are obtained by optimization with use of the
mean of individual data. Differences between meals were tested using a
GLM procedure for repeated measures: P < 0.05 MP vs. FMP; P < 0.05 MP vs.
SMP; *P < 0.05 FMP vs. SMP.
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DISCUSSION |
The purpose of this study was to model the specific effect of
dietary energy nutrients (fat or sucrose) on the dynamic exchange of
dietary N between splanchnic and peripheral organs and on its regional
metabolism in the fed state. The consumption of three different meals,
MP, FMP, and SMP, enabled the study of three combinations between the
insulin and AA levels. 1) MP, which was rapidly absorbed,
led to the rapid, high, and transient appearance of dietary N in the
splanchnic and peripheral free AA pools without insulin activation;
2) FMP, which was more slowly absorbed, led to a slightly
slower and lesser appearance of dietary N in the splanchnic and
peripheral free AA pools without insulin activation; and 3)
SMP, which was very slowly absorbed, reduced to a greater extent the
speed and amplitude of dietary N appearance in free AA pools, with a
concomitant acute hyperinsulinemic postmeal response (25).
Compartmental analysis of these data thus enabled simulation of the
partitioning kinetics of retained dietary N between free AA and
proteins in both the splanchnic and peripheral areas (Fig. 8). The model thus
made it possible to determine that the higher whole body retention of
dietary N experimentally observed in the presence of sucrose was
associated with its higher and prolonged sequestration into the
splanchnic tissues, leading to a delayed release to the periphery.
Furthermore, it provided new evidence indicating that fat transiently
enhances splanchnic dietary N incorporation into protein, an outcome
that could not be deduced directly from experimental results. The
physiological relevance of our model is supported by the consistency of
its predictions with respect to our current knowledge of the system and
by its ability to discriminate between different nutritional
conditions, since it highlighted significant differences between the
meals. The modeling approach thus proved to be a useful tool with
possible applications to several nutritional conditions and with the
aim of optimizing diet formulation in various physiological conditions.

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Fig. 8.
Kinetics of the distribution of retained dietary
N (between free and bound AA in visceral or peripheral regions) and of
lost dietary N (both deamination and ileal losses). Values (areas in
the figure represent respective parts of total dietary N retention or
loss) were obtained by optimization with use of the mean of individual
data after MP (A), FMP (B), and SMP
(C) meals.
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This modeling tool enabled a precise description of the dynamics of
dietary N distribution during the postprandial state. The model
simulated rapid emptying of the gastric N content after the ingestion
of a pure protein (MP) meal with an emptying half-time of ~20 min,
which was close to previous experimental results (10, 28).
Gastric emptying was more delayed in the presence of additional dietary
sucrose than with additional fat, with a time lag of 80 min in the
emptying half-time after SMP vs. MP. This result seems particularly
relevant, because a previous study involving two meals with the same
protein and sucrose content as MP and SMP had reported a similar delay
in the gastric emptying profile (30). Consistent with
previous findings in the literature, summarized in Table
3, the model predicted a predominant
splanchnic uptake of dietary N during the early postprandial phase,
which modulated the delivery of dietary AA to peripheral tissues
(3, 23, 27, 29, 35, 50). This is in agreement with the
idea that the acute anabolic effect of orally ingested AA occurs
primarily in the splanchnic area (13, 17, 38, 44), whereas
muscle proteins are of little importance to the rapid replenishment of tissue proteins after ingestion (33). According to several
studies, the first-pass splanchnic extraction (i.e., retention plus
deamination) of AA after the administration of a complete AA mixture
accounts for between 30 and 70% of ingested AA, depending both on the
AA considered and the duration of the experiment (45, 50,
51). Consistent with these results, we report the splanchnic
extraction of ~46% of dietary input 8 h after the MP meal. This
value rose to ~56% after the SMP meal, in line with previous studies
that reported a splanchnic utilization of 58% for Phe in humans
continuously fed a complete mixed meal (7) and 53%
(averaged value for Leu, Lys, Phe, and Thr) after the constant
intragastric administration of a complete mixed meal in piglets
(38). Moreover, in agreement with the findings in piglets
(36, 37) and in dogs (50, 51), the model
predicted incorporation into splanchnic protein, which varied as a
function of the meal between 20 and 30% of ingested N at 6 h. The
amount of dietary N reaching peripheral tissues was thus predicted to
be reduced with the addition of dietary sucrose, and to a
lesser extent with that of fat, as a consequence of both delayed
absorption kinetics and an increase in temporary splanchnic retention
and anabolism. Thus predicted peripheral uptake reached 37% of dietary
N 4 h after MP ingestion but was significantly lower at 21% at
the same time point after SMP. Consistent with this latter value, only
18% of available branched-chain AA had previously been estimated to be
taken up by muscle during the first 4 h after ingestion of a
single mixed meal (22). The predicted relative
contribution of peripheral proteins to the total amount of proteins
synthesized 8 h after a meal reached ~65% after MP and
decreased to ~42% after SMP. This is consistent with previous
findings that showed that protein synthesis in muscle made only a
minority contribution (30-50%) to the whole body anabolic response after a mixed meal (31).
Dietary AA and insulin are considered to play important roles in
promoting postprandial protein anabolism, but the isolated and/or
concomitant contribution of hormonal and substrate factors to the
protein anabolism associated with the fed state still requires clarification (44). An interesting outcome of these model
predictions was the determination of dietary N replenishment kinetics
of free AA pools in the tissues. These pools can be considered as
buffer areas capable of an immediate response to acute variations in N
intake, whereas protein synthesis systems have limited capacities to
deal with an AA excess (9, 21). As a direct consequence, a
rapid increase in the free AA pool leads to the saturation of synthesis
capacity and exposes transiently stored AA to oxidation (34). Consistent with these findings, our model correctly
predicted that the amount of dietary N incorporated into SP after
7 h would be negatively correlated with the height of the SA peak.
Fat and sucrose are thus associated with a temporary increase in
splanchnic accretion during the first postprandial hours, as a
consequence of delayed dietary N gastric emptying and the resulting
flattened SA appearance profile. The idea of a central and regulatory
role of SA was further supported by the results of the sensitivity analysis, as the rates of disappearance from SA
(k9,4, the transfer rate to body urea,
k5,4, the rate of delivery to the periphery, and
k7,4, the transfer rate to SP) exerted the most
influence on the whole body retention and incorporation of dietary N
into SP and PP. Sensitivity analysis further indicated that, if sucrose was present in the meal, the gastric emptying rate
(k2,1) and splanchnic protein synthesis rate
(k7,4) exerted an increased influence on the
metabolic fate of dietary N. The slowing down of gastric emptying,
which led to a delayed and flattened appearance of dietary N into SA,
and the enhanced incorporation of dietary N from SA into SP may both,
therefore, be involved in the dietary N sparing effect of dietary
sucrose. By analogy with dietary CHO, Boirie et al. (8)
had already developed the concept of "slow" and "fast" proteins
according to the rate at which dietary proteins are digested and
absorbed from the gut, and they concluded that the resulting pattern of
aminoacidemia (amplitude and duration) affects postprandial protein
synthesis, breakdown, and deposition. It has also been reported in
food-deprived chicks that liver protein synthesis was more stimulated
in animals fed protein plus sucrose, and to a lesser extent protein
plus fat, than protein alone (48). In contrast, in the
peripheral zone, the incorporation of dietary N into protein is
positively correlated with the free AA level. Consistent with our model
predictions, it has already been reported that AA concentrations are
the principal determinant of AA uptake across human forearm tissue
during a protein meal (1). The key role played by the
kinetics of dietary AA appearance in the splanchnic bed and peripheral
area regarding modulation of their utilization for anabolism emphasizes
the importance of studying N metabolism in the fed state using a
non-steady-state approach.
Moreover, the presence of dietary sucrose induced acute
hyperinsulinemia that was not observed after MP or FMP
(25). Insulin has been reported to increase whole body
protein synthesis in the concomitant presence of AA (12),
but studies concerning regional AA metabolism have generally failed to
demonstrate a stimulating effect of insulin on muscle tissue protein
synthesis, a paradigm for the largest protein pool in the body
(16, 17). Likewise, some studies in mice have failed to
demonstrate a muscular anabolic effect of the ingestion of starch plus
casein, despite the anabolic effect of this meal in the liver and
gastrointestinal tract (33). Furthermore, the well-known
peripheral hypoaminoacidemic effect of systemic hyperinsulinemia can be
explained by the rapid primary action of this hormone in the splanchnic
region (31, 40). Taken together, these and other data
suggest that splanchnic tissue may be involved mainly in the increase
in protein synthesis associated with insulin secretion
(17), because any acute hormonal effect would have an
earlier impact on fast- rather than on slow-turning over proteins
(39). Thus the addition of CHO to a pure protein meal has
been shown to enhance protein synthesis in the gut, and this increase
was found to be related to the postprandial insulin response
(19). Similarly, albumin synthesis has been reported to be
under the regulation of insulin (16, 17, 39, 44). In this
respect, the hyperinsulinemia observed after SMP was predicted to be
associated with a stronger splanchnic anabolic response compared with
both MP and FMP and a weaker peripheral response compared with MP.
However, despite the decreased dietary N availability in peripheral
tissues after SMP ingestion, the fraction of the peripheral free AA
rate of appearance that is incorporated into protein, i.e., peripheral
protein synthesis efficiency (PSE), was stimulated by hyperinsulinemia,
as previously reported elsewhere (5). Peripheral PSE thus
increased from ~25% after MP and FMP to 31% after SMP ingestion.
These values are in the same range (~30%) as those previously found
for muscle PSE after the ingestion of an AA mixture (43,
45). However, the higher PSE after SMP did not compensate for
concomitant lower substrate availability, thus leading to a lower
incorporation of dietary N into PP compared with MP. We therefore
conclude that dietary AA alone stimulate muscle protein anabolism after
the ingestion of a pure protein meal, solely by increasing the
availability of substrate for protein synthesis, as previously reported
(41). Insulin has an additional effect by stimulating
anabolism but to a lesser extent in muscle than in the splanchnic area.
In fact, the regional differential effect of insulinemia and
aminoacidemia depends concomitantly on the size and
fractional synthetic rate (FSR) of the protein pool involved. Because
of the small size and high FSR of the splanchnic protein pool,
modulation of its synthesis efficiency is the principal determinant of
dietary N incorporation. In contrast, the small FSR but considerable
pool size of peripheral tissues results in an ability to incorporate
substrates, which is mainly dependent on their availability.
In conclusion, our model proved to be accurate in discriminating
between different nutritional conditions and can be used as a
predictive tool to highlight the acute postprandial mechanisms occurring in tissues and regulating the dynamic transfer of dietary N
between organs. The replenishment dynamics of free AA pools and the
synergistic effect of hormone secretions appear to play a primordial
role in dietary N utilization by tissue proteins. Indeed, the slowing
of gastric emptying and the temporary increase in splanchnic retention
and anabolism observed after the SMP meal (which is more similar to the
probable physiological case of mixed-meal ingestion) are likely to
feature the processes involved in N homeostasis in the context of
discontinuous intake of nutrients (31). These processes
would allow the sparing of dietary N from deamination through the
temporary "storage" of ingested AA in the labile splanchnic protein
pool (17, 34, 36, 37, 46), while simultaneously buffering
the peripheral tissues from excessive changes to free AA concentrations
(1, 2), thus maintaining long-term homeostasis by
spreading the release of meal-derived AA into the body over a longer
period of time (18, 34, 35). We demonstrate that the
reduced deamination of dietary protein observed experimentally during
the postprandial nonsteady state in the presence of CHO was due to a
higher dietary N incorporation into the splanchnic protein, whereas
release to the peripheral area was markedly reduced. The anabolic
action of insulin was particularly efficient in the splanchnic tissues,
which are exposed to the highest concentrations of exogenous AA and are
capable of responding with substantial protein synthesis because of
their high protein turnover. In contrast with our previous inability to
observe any fat effect on whole body dietary N retention from
experimental data, in this case, we were able to detect a
transient improving effect of fat on the incorporation of dietary N
into splanchnic protein during the first postprandial hours. This was
due to the delays in gastric emptying and dietary AA absorption, but
the absence of insulin secretion led to a rapid release of dietary AA
and their subsequent deamination. Finally, our model is little invasive
and could usefully be confronted with several conditions to optimize
food composition as a function of physiological and nutritional status
(exercise, aging, hormonal impairment).
 |
ACKNOWLEDGEMENTS |
We acknowledge the contribution of the modeling work group at the
Institut National Agronomique Paris-Grignon for stimulating discussions
during the course of this work.
 |
FOOTNOTES |
This work was supported by Arilait Recherches (Paris, France).
Address for reprint requests and other correspondence: H. Fouillet, UMR de Physiologie de la Nutrition et du Comportement Alimentaire, Institut National Agronomique Paris-Grignon, 16 rue Claude Bernard, F-75231 Paris Cédex 05, France
(fouillet{at}inapg.inra.fr).
The costs of publication of this
article were defrayed in part by the
payment of page charges. The article
must therefore be hereby marked
"advertisement"
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 27 November 2000; accepted in final form 20 March 2001.
 |
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