Download Supplementary Text - Overview of nutrition for endurance athletes

Supplementary Text - Overview of nutrition for endurance athletes
The body of an athlete converts large amounts of energy. Therefore, a comprehensive model
of energy conversion and metabolism in the athlete should in the future also include nutrition
intake and absorption. Some relevant and interesting aspects to take into consideration when
modeling nutrition for athletes are discussed in this section.
With regard to nutrition intake, recent reviews on nutrition composition before, during
and after exercise [1] and on the timing of nutrition in relation to exercise [2] offer the
following
general
recommendations.
Energy
and
macronutrient
needs,
especially
carbohydrate and protein, must be met during times of high physical activity to maintain body
weight, replenish glycogen stores, and provide sufficient protein to build and repair tissue. Fat
intake should be sufficient to provide the essential fatty acids and fat-soluble vitamins while
also contributing energy for weight maintenance. Especially during exercise a steady supply
of carbohydrates to replenish muscle glycogen stores and to support energy metabolism is
important. In line with the importance of carbohydrates supply, the influence of glycaemic
index (GI), a measure to functionally rank carbohydrates based on their actual postprandial
blood glucose response, on exercise performance has received much attention. However,
although at the biochemical level there has been consistent evidence that altering the GI may
change fat and carbohydrate oxidation during exercise [3], there is no consensus on whether
consuming carbohydrates of differing GI improves endurance performance [4]. It has been
suggested that the glycaemic load (GL), a relatively novel concept in the area of sports
nutrition which considers the overall glycaemic effect of a diet and not the amount of
carbohydrate alone, may be a better predictor of glycaemic responses than the GI alone [5].
While carbohydrates obviously are important, of special interest is that different nutrients
have been found to interact in their effectivity to achieve a prime main target i.e. inproving
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glycogen storage which is the major endurance-limiting substrate. Thus, Ivy et al. [6] found
that the addition of protein (Pro) to a carbohydrate (CHO) supplement enhanced aerobic
endurance performance above that which occurred with CHO alone. Studies of Zawadzki et
al. [7] and Ivy et al. [8] comparing CHO, Pro, and CHO-Pro supplements suggest that a CHOPro supplement is more effective for the rapid replenishment of muscle glycogen after
exercise than a CHO supplement of equal CHO or caloric content, or than a supplement of
Pro alone. This advantage seems to hold even when the caloric content of the CHO-Pro
mixture is lower than that of the CHO mixture alone, demonstrating the presence of a true
interaction effect [9]. Adding Pro to a low-calorie CHO sports drink may be an effective
strategy to enhance aerobic capacity while limiting carbohydrate and caloric consumption
[10]. The CHO-Pro interaction effect is probably a result of the interaction of CHO and Pro
on insulin secretion [7]. Van Loon et al. [11] found that a mixture drink of wheat protein
hydrolysate, free leucine, and phenylalanine, appeared to generate the maximal insulinotropic
effect when co-ingested with carbohydrate. Further contributing to this issue, Miller et al. [12]
examined
the
metabolic
response
of
hormones
(insulin,
glucagon,
epinephrine,
norepinephrine, growth hormone, testosterone, and cortisol) and metabolites (glucose, lactate,
free fatty acids, and selected amino acids) in plasma to a provision of mixed proteincarbohydrate supplementation during endurance exercise. Alternatively combining CHO with
fat in interventions aiming to increase fat availability before exercise has been shown to
reduce carbohydrate utilization during exercise, but does not appear to result in ergogenic
benefits [13]. Data from these and future nutrition studies focusing on GI, GL, carbohydrates,
proteins, fat as well as their interactions, can be used to construct and calibrate relevant
sections of a computer model focusing on the influence of nutrion in the athlete.
Since the gastrointestinal tract is the site of nutrient absorption, it is a key organ also
for the athlete. In an excellent review, Gisolfi [14] demonstrates that the gut can meet the
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demands of prolonged severe exercise and can even show signs of adaptation to exercise
training but can also signal conditions of impending injury. During severe exercise,
splanchnic blood flow is markedly reduced and intestinal permeability can increase by
opening tight junctions, which is considered the central mechanism initiating immunologic
and inflammatory events that can severely impair gut structure and function. In a recent
review by de Oliveira et al., gut ischemia was confirmed as the probable main cause of
nausea, vomiting, abdominal pain and (bloody) diarrhea experienced by athletes [15]. Both
reviews in fact underline the importance of nutrition that supports gastrointestinal health and
integrity. Therefore, it is relevant to also consider the possible beneficial role of
gastrointestinal microbial metabolism in relation to sports nutrition. Not only does gut
bacterial metabolism contribute significantly to the body’s energy balance [16], but one of
the metabolic products, the short-chain fatty acid (SCFA) butyrate, is a key gut healthpromoting compound [17,18]. The absolute and relative gut bacterial synthesis rates of
butyrate and the other SCFA acetate (a precursor of the important energy substrate acetylCoA) and propionate (a precursor of hepatic gluconeogenesis) depend on the gut microbial
flora composition and can also be influenced by dietary interventions (i.e., prebiotics) [19-21].
The link between nutritional composition and desired gut bacterial SCFA production, is
therefore necessarily complex, requiring a selective measurement of microbial metabolites
and a quantification of the involved metabolic fluxes in connection with analysis of the gut
microbiota composition. Mathematical modeling is needed to interpret these data. However,
the human gut microbiota consists of hundreds of microbial species. To study metabolism in
this complex situation a workable alternative is an in vitro model containing the microbiota.
To illustrate the combination of measurements in an in vitro model with analysis of
measured data by mathematical models, we briefly discuss our recent study of the conversion
of starch into the main short chain fatty acids (SCFA) in human gut microbiota using isotope
3
labeling experiments. RNA-Stable Isotope Probing (SIP) analysis has identified the main
bacterial species involved in [U-13C] starch fermentation [22]. To determine microbial
metabolites and SCFA production, we used Nuclear Magnetic Resonance (NMR) analyses
[23] in combination with a computational modeling approach for metabolite and flux analysis
[24].
For interpretation of the data, a metabolic model (Supplementary Figure 1) was
assembled from literature information [25, 26] and implemented using ordinary differential
equations (ODEs). The necessary isotope labeling experiments were done using an in vitro
model of the human gut (TIM-2) [27, 28], which facilitated to measure the time course of
isotope incorporation in the key SCFA acetate, propionate and butyrate by mass spectrometry
(MS) and NMR spectroscopy, in addition to providing the isotopically labeled biomass for
RNA-SIP analyses.
With the described approach, we successfully quantified multiple metabolic
parameters defining the metabolic fluxes in the microbiota using global parameter
optimization. The RNA SIP analyses [22] in combination with the metabolic data suggest
that metabolic cross-feeding occurs in the system, where populations related to Ruminococcus
bromii are the primary starch degrader, while those related to Prevotella spp., Bifidobacterium
adolescentis and Eubacterium rectale might be further involved in the trophic chain, thus
giving insight in nutrition-controlled, microbe-mediated gut metabolism.
This approach
demonstrates how an in vitro model of the biochemistry can be combined with computational
analysis. Given the enormous complexity of microbial metabolism in the gut, this is a viable
alternative to complete in silico modelling.
Legend to Supplementary Figure 1.
Carbon transition model of the gut bacteria. The parameters estimated in our study are:
relative splitting factors of metabolic flow (W, X, Y and Z), labeled influx from starch (J in),
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unlabeled influxes (JdilA and JdilB), back flux from acetate to acetyl CoA (Jxch), CO2 leaving the
system (Jout) and dialysis rate constants (kpropionate, kacetate and kbutyrate). This model was used to
derive metabolic fluxes from stable isotope measurement data obtained with a human
microbiota in an in vitro system [24].
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