University of Groningen
Dietary carbohydrate digestion and fermentation
Wang, Hongwei
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RIJKSUNIVERSITEIT GRONINGEN
Dietary carbohydrate digestion and fermentation:
Relevance for prevention of type 2 diabetes
Hongwei Wang
王红伟
Promotor: Prof. dr. R. J. Vonk
Design and layout: Hongwei Wang
Pringting: WÖHRMANN PRINT SERVICE, Zutphen, the Netherlands
Cover art: “scientific mechanism and the theme of abstract artwork”,
by Zosia and Hongwei
ISBN: 978-90-367-4500-0
©2010 Copyright Hongwei Wang
All rights reserved. No part of this publication may
be reproduced, stored in a retrieval system, or
transmitted, in any form or by any means without
the prior written permission of the author.
RIJKSUNIVERSITEIT GRONINGEN
Dietary carbohydrate digestion and fermentation:
Relevance for prevention of type 2 diabetes
Proefschrift
ter verkrijging van het doctoraat in de
Medische Wetenschappen
aan de Rijksuniversiteit Groningen
op gezag van de
Rector Magnificus, dr. F. Zwarts,
in het openbaar te verdedigen op
maandag 13 september 2010
om 16.15 uur
door
Hongwei Wang
geboren op 10 december 1975
te Jiangchuan, Yunnan, China
Promotor:
Prof. dr. R. J. Vonk
Beoordelingscommissie: Prof. dr. K. Verbeke
Prof. dr. T. Preston
Prof. dr. G. van Dijk
ISBN:
978-90-367-4500-0
To my grandparents.
Paranimfen: Jolien van der Berg
Martijn Dijkstra
CONTENTS
SUMMARY ...................................................................................................... 1
AIM OF THIS THESIS .................................................................................... 6
SCOPE OF THIS THESIS................................................................................ 7
RESULTS AND DISCUSSION ....................................................................... 9
REFERENCES................................................................................................ 19
APPENDIX 1
Colonic fermentation of non-digestible carbohydrates of a previous evening
meal increases tissue glucose uptake and moderates glucose-associated
inflammation ................................................................................................... 30
APPENDIX 2
Manipulation of colonic short-chain fatty acid production: relevance for the
prevention of insulin resistance....................................................................... 64
APPENDIX 3
A curve fitting approach to estimate the extent of fermentation of indigestible
carbohydrates .................................................................................................. 96
APPENDIX 4
Type of non-digestible carbohydrate affects plasma and urine short chain fatty
acid profiles in healthy volunteers ................................................................ 114
SAMENVATTING....................................................................................... 141
ACKNOWLEDGEMENTS .......................................................................... 144
CURRICULUM VITAE ............................................................................... 147
LIST OF PUBLICATIONS .......................................................................... 148
ABBREVIATIONS ...................................................................................... 150
SUMMARY
SUMMARY
Dietary carbohydrate consumption has been related to the prevention of type 2
diabetes (T2DM). Two major processes have been suggested as the underlying
mechanisms: decrease of postprandial blood glucose response and/or
fermentation of non-digestible carbohydrates. The fermentation process was
thought to play a role through its fermentation products short-chain fatty acids
(SCFAs, mainly acetate, propionate and butyrate).
We observed that in healthy men non-digestible carbohydrates taken in an
evening meal were able to diminish postprandial blood glucose response,
increase peripheral insulin sensitivity and suppress the glucose induced proinflammatory cytokines interleukin 6 (IL 6) and tumor necrosis factor α (TNF-α)
the next morning (second meal effect). The increased breath hydrogen and the
higher plasma butyrate level strongly suggested that colonic fermentation may be
responsible for these beneficial effects, with butyrate as a possible intermediate
factor (Chapter 1).
As decreased insulin sensitivity and increased pro-inflammatory cytokines are
related to the development of T2DM and cardiovascular disease, our results
suggested that colonic fermentation might be relevant for the prevention of these
chronic diseases and this preventive effect might be special for non-digestible
carbohydrates, which have a specific SCFAs profile. The literature about the
relevance of SCFAs towards insulin sensitivity and the possibility of manipulating
the colonic fermentation to produce the optimal SCFAs profile was reviewed.
Colonic fermentation can be manipulated by changing diet, adjusting smallintestinal and colonic transit time, applying small intestinal enzyme inhibitors, and
modifying the specificity and activity of colon microbiota. We hypothesized that
the insulin sensitizing effect of SCFAs is related to the SCFAs profile.
Manipulation of the SCFAs profile could be a target for a dietary approach to
reduce insulin resistance and control insulin resistance related diseases. The
(functional) foods which produce the SCFAs profile similar to that of whole grain
1
SUMMARY
barley kernel might be potent in prevention of insulin resistance and T2DM
(Chapter 2).
The metabolic effect of dietary carbohydrates is determined by the extent to
which carbohydrates are digested in the small intestine and fermented in the
colon. We developed a mathematic model to evaluate in vivo the degree of
digestion and fermentation of foods rich in carbohydrates. This model provided
us a non-invasive and feasible approach to evaluate the contribution of the
digestion and fermentation processes to the final overall metabolic effect .With
13
C labeled foods available, we may obtain a specific fermentation index (FI) for
each carbohydrate food or diet. The fermentation index together with the
glycemic index (GI) would be able to provide comprehensive information about
the biological characteristics of carbohydrate rich foods (Chapter 3).
The fermentability of the various non-digestible carbohydrates including
dietary fibers is not documented in detail until now. To achieve this, analytical
method was developed to monitor the SCFAs profile in peripheral blood and
urine after intake of different non-digestible carbohydrates. We used this method
to test the feasibility of changing the SCFAs profile by dietary substrates. The
test food was labeled with stable isotopes, which enabled us to link the
fermentation products to the test food itself. We found that SCFAs profiles in
plasma and urine
could be manipulated
using various
non-digestible
carbohydrates sources (Chapter 4).
In summary, with these new techniques to monitor colonic fermentation, we
were able to further investigate the metabolism of non-digestible carbohydrates
in relation to prevention of insulin resistance/type 2 diabetes. The association
between colonic fermentation and insulin sensitivity has been supported by
accumulating evidence. Manipulation of colonic fermentation with dietary
carbohydrates seems a promising approach to prevent and control insulin
resistance and its related diseases, especially T2DM. The design of a second
meal effect study is one of the approaches to evaluate the acute beneficial effect
of foods rich in non-digestible carbohydrates.
2
INTRODUCTION
INTRODUCTION
In 2000, the total number of people worldwide with diabetes was estimated to be
171 million, which is predicted to double by 2030, the corresponding prevalence
being 2.8% and 4.4%, respectively (1). The epidemic of obesity and T2DM was
suggested to be related to the consumption of a high fat, high protein, and low
carbohydrate diet, as well as lack of physical exercise and the sedentary lifestyle.
Among all the strategies to cope with this issue, efforts have been made to find a
dietary solution for this epidemic (2).
What should be of concern for such a diet is calories (macronutrients intake)
as well as properties of individual nutrients. Dietary carbohydrates, as the major
component of our diet, have attracted increasing attention. It was found that
enhancing the proportion of dietary carbohydrates could decrease the risk of
developing T2DM. The T2DM prevalence is lower in people adhering to a
vegetarian diet than that of a normal diet (3). However, different carbohydrates
may have variable effects on energy and substrate metabolism in humans.
Carbohydrate that significantly increase postprandial blood glucose and in
parallel blood insulin is adverse to glucose tolerance. Therefore, what should be
concerned is not only the amount of carbohydrate in the diet, but also the
properties of the dietary carbohydrates (4;5).
Different types of carbohydrates are characterized by their ability to induce
postprandial glycemic response. This finding resulted in the creation of the
concept of glycemic index (GI) (4). In addition, dietary carbohydrates are also
different in content and types of non-digestible carbohydrates including nonstarch polysaccharides, resistant starch (RS), non-digestible oligosaccharides
and sugar alcohols (6). The non-digestible part of the dietary carbohydrates as
well as part of the digestible carbohydrates which escape the digestion in the
small intestine will reach the colon and will be (partly) fermented by colonic
microbiota (7;8). The physiological effect of colonic fermentation is attracting
more and more attention.
3
INTRODUCTION
For a long time, the colon was regarded merely as a storage organ which
absorbs water and some minerals and store faeces waiting to be evacuated (9).
The importance of the enormous number of microbiota residing in the colon and
their complex activities has just emerged. The number of the microbiota is 1014
which is ten times that of the total human cells (10), and the number of genes is
100 times that of the host (11). The role this complicated colonic system can play
on human health has just been recognized. Colon function has implications not
only for the digestive tract, but also for the peripheral organs such as liver,
adipose tissue and muscle through various signaling factors derived from the
colon(7;12).
Colonic fermentation is a complicated process which produces SCFAs,
branch-chain fatty acids (BCFAs), CO2, H2, CH4 and other products (7). SCFAs
are believed to be the main intermediates between colonic fermentation and their
peripheral effects (7;8). Acetate is one of the principal SCFAs in the colon which
increases cholesterol synthesis after absorption. Propionate is involved in
gluconeogenesis acting as both an inhibitor and a promoter, and has been
shown to inhibit cholesterol synthesis. Butyrate is mainly used by colonocytes as
an energy source and plays a role in the maintenance of colonic homeostasis.
Butyrate is also involved into other beneficial effects such as anti-inflammation,
anti-carcinogenesis and suppressing oxidative stress (13).
Given the emerging relevance of the colonic system to human health, colonic
fermentation must be taken into account when effects of dietary carbohydrates
are concerned. As dietary carbohydrates are involved in prevention and
treatment of T2DM (2), the study of colonic fermentation of dietary carbohydrate
may provide valuable information for T2DM control and prevention.
Whole grain foods might be low in GI and rich in non-digestible
carbohydrates, and their active compounds may play an additional role, singly or
synergistically (14). These characteristics of whole grain make it attractive as
part of a preventive strategy towards T2DM and other life style related chronic
diseases (16). Therefore, we used barley as the test food.
4
INTRODUCTION
There are at least three approaches to evaluate the metabolic effect of whole
grain: a acute postprandial observation, b the design of the second meal
effect, and c long term interventional trials. As we were interested in the acute
effect of colonic fermentation, we were evaluating the effect of whole grain food
from a second meal effect point of view because the design of this type of study
enables us to differentiate the effect of colonic fermentation from that resulting
from low GI of the test food. Colonic fermentation has been suggested as one of
the mechanisms that induce the second meal effect (17-23)
In our studies we were focusing on colonic fermentation of non-digestible
carbohydrates, especially in relation to the production of SCFAs.
5
AIM OF THIS THESIS
AIM OF THIS THESIS
1. To investigate the effect of colonic fermentation of non-digestible
carbohydrates using the overnight second meal design;
2. To develop techniques which can be used to characterize fermentability of
non-digestible dietary carbohydrates and to monitor the SCFAs profile;
3. To explore a dietary strategy based on colonic fermentation of non-digestible
dietary carbohydrates for increasing insulin sensitivity.
6
SCOPE OF THIS THESIS
SCOPE OF THIS THESIS
To achieve the study aims, we designed and conducted the following
experiments.
1. The underlying mechanisms of the overnight second meal effect and the
effect of colonic fermentation
In a randomized crossover study (Appendix 1), the overnight second meal effect
of whole barley grain compared with white bread was studied in a group of
healthy men (n=10). The mechanisms of the overnight second meal effect were
explored, and the relevance of colonic fermentation was investigated.
2. The relevance of colonic fermentation to T2DM prevention
The relevance of SCFAs towards insulin sensitivity, and the possibility of
manipulating colonic fermentation to produce the optimal profile of SCFAs were
reviewed. The potential of preventing insulin resistance and T2DM through
manipulation of colonic fermentation was discussed based on our own results
and the available evidence in the literature (Appendix 2).
3. Mathematical model approach to evaluate the degree of digestion and
fermentation of food rich in non-digestible carbohydrates
A study was designed and conducted in healthy volunteers (n=17) to investigate
the possibility that obtaining information about food digestion and fermentation in
a non-invasive and feasible way (Appendix 3).
13
was used as a model food, breath H2 and
13
C labeled whole grain barley
CO2 were monitored and a
mathematical model was developed to estimate the degree of digestion and
7
SCOPE OF THIS THESIS
fermentation of dietary carbohydrate.
4. The plasma and urine SCFAs profiles of various non-digestible
carbohydrates
The plasma and urine SCFAs profiles of non-digestible carbohydrates was
investigated in healthy volunteers (n=5) (Appendix 4). This method allowed
monitoring of SCFAs profiles in plasma and urine after ingestion of food rich in
non-digestible carbohydrates. This study showed the possibility to manipulate the
plasma and urine SCFAs by using various non-digestible carbohydrates sources.
8
RESULTS AND DISCUSSION
RESULTS AND DISCUSSION
1. The second meal effects
The effects of an evening meal on the metabolism of the subsequent breakfast
were investigated (Appendix 1). Compared with white bread evening meal (WB),
the barley kernel evening meal (BA) improved glucose tolerance in healthy
young men the next morning. The plasma glucose response after the glucose
drink was 29 % lower after the BA evening meal (P = 0.019). The change of
insulin response was not statistically significant. The diminished postprandial
glucose response and unchanged insulin response after BA was in line with
results of other authors (17;18;21;22).
The blood glucose kinetics during oral glucose tolerance test (OGTT) was
monitored using the double-label stable isotope technique. This technique
enable the measurement of systemic rate of appearance of total glucose (RaT),
endogenous glucose production (EGP), systemic rate of appearance of
exogenous glucose (RaE), and glucose clearance rate (GCR), which reflect the
total influx rate of glucose into blood circulation, the glucose influx from liver, the
glucose influx from intestinal absorption, and the rate that glucose is cleared from
blood circulation, respectively.
It was found that the GCR after BA was
significantly higher (23 %, P = 0.016) than that of WB, but RaT, EGP, and RaE
were the same. The net blood glucose was determined by the influx and the
clearance. As the influx, i.e., RaT, was the same, whereas the clearance, i.e.,
GCR, was higher for BA than that of BW, the net blood glucose should be lower
for BA than that of WB. This was exactly what was observed by the
measurement of blood glucose.
Because the insulin response was the same for BA and WB, the increased
GCR could be the outcome of improved insulin sensitivity, or the outcome of
other factors which can play an insulin-like role. It was proposed that insulin
9
RESULTS AND DISCUSSION
sensitivity can be measured during an OGTT (24). According to this approach we
calculated the insulin sensitivity index (ISI) after BA and WB. We found a higher
ISI for BA compared with that of WB.
It has been reported that the second meal effect of a barley evening meal
compared with a white bread evening meal was mediated by suppressed free
fatty acids (FFA) (20-23;25) and delayed gastric emptying after the barley meal
(20). We didn’t find different FFA for the two evening meals, and the gastric
emptying was not expected to be different because we observed the same RaE
for both BA and WB. Also we observed a higher plasma butyrate for BA (103 %
higher 0-2 h incremental area under the curve (iAUC), P = 0.041) which indicated
a higher colonic fermentation, as also shown by higher breath hydrogen for BA.
Therefore, the improved GCR might be induced by butyrate. We could not
conclude from our results a direct causal relationship between butyrate and the
observed beneficial effect. As SCFAs are the main products of colonic
fermentation, they were the focus of this thesis, but other colonic fermentation
related factors could also be possible mediators, e.g. angiopoietin-like protein 4
(also know as fasting-induced adipose factor), lipopolysaccharide (LPS), incretin
hormones (26), and transformation of dietary compounds into bioactive products
(27).
Furthermore, moderation of plasma pro-inflammatory cytokines was seen for
BA. After the WB, the postprandial mean IL 6 (WB vs. BA: 19.7 ± 5.1 vs. 5.1 ±
0.7 pg/mL, P = 0.024) and tumor necrosis TNF-α (WB vs. BA: 7.8 ± 2.1 vs. 5.3 ±
1.6 pg/mL, P = 0.008) levels were significantly higher compared with the BA, with
the values after the BA almost the same as the baseline values. Proinflammatory cytokines can deteriorate the insulin sensitivity (28). This should not
be the case in our study since the significant difference of both IL 6 and TNF-α
occurred during postprandial 0-4 h rather than 0-2 h, although the change of the
pro-inflammatory profile in our study might have relevance to the insulin
sensitivity in the long run. In contrast, except for glucose, the significant
differences for butyrate, and GCR occurred during postprandial 0-2 h. The iAUC
10
RESULTS AND DISCUSSION
for glucose remained significantly different after 2 h. It was shown that high blood
glucose can induce pro-inflammatory cytokines secretion (29). Therefore, the
higher pro-inflammatory cytokines could be resulted from the higher blood
glucose, whereas the moderated pro-inflammation might be due to butyrate or
other fermentation related factors
In order to understand the underlying mechanisms in relation to the
fermentation processes we addressed the following questions:
-a. What was the fermentable substrate when given barley kernels?
-b. What was the fermentation site?
-c. Why did the fermentation increase following the ingestion of the breakfast?
a. What was the fermentable substrate when given barley kernels？
We have to realize that the oral-cecal transit time (OCTT) plays in important role
in answering this question. The OCTT for similar barley kernel test meals was 4
h (Appendix 3). In this study, the evening meal was ingested for 10 h when the
subsequent breakfast was ingested. But during sleep, the OCTT was possibly
slower than when subjects are active. We didn’t know how slow it was in this
experimental situation since we didn’t monitor the OCTT to avoid disturbing
sleeping, but an OCTT of 10 h is possible. In addition, it was found that barley is
rich in soluble dietary fiber and RS, and the relative intact physical structure of
barley kernel may reduce the availability of the substrate to the digestive
enzymes, subsequently increased the undigested starch reaching colon (30).
This implies that the carbohydrate fraction reaching the colon to be fermented
might be more than that estimated based on the RS and dietary fiber (DF)
measurement.
Furthermore, most of the methods for OCTT measurement reflect only the
time when the head of the digesta reaches caecum. It takes longer time for all
11
RESULTS AND DISCUSSION
the digesta to arrive at the caecum. The realistic situation could be that there is
always some digesta at the end of the ileum 10 h after intake of the evening
meal. In addition, the way the digesta transfers from the terminal ileum to
caecum might imply that there is still some digesta waiting at the end of the ileum
to be propelled to the caecum. The ileocolonic transit increased postprandially
and slowed down during fasting (31). Therefore, the fermentable substrate in the
morning is possibly still from the evening test meal. This should be proven in a
future study by applying stable isotope labeled evening meal and measuring the
OCTT of the evening meal with feasible methods, for example, a smartpill (32) ,
a wireless device which monitors luminal pH and pressure.
b. What was the fermentation site?
Upon the arrival of the digesta at the colon, most of the fermentation occurs at
the ascending colon. It was found that 12 h after intake of a solid meal, most of
the ingested meal was in the ascending colon (33). The digesta takes about 13 h
(34) to pass the ascending colon. In addition, the movement of the colon could
be activated by eating. The colon movement would push digesta to distal sites of
the colon. The microbiota located in different colon sections is different. The
digesta composition disfavored by microbiota in caecum might be the favorable
substrate for microbiota residing in the transverse colon and beyond. There was
also cross-feeding between microbiota
which is especially important for the
production of butyrate (35-37). It was therefore reasonable to assume that during
the OCTT in the morning, fermentation was increased not only in the caecum,
but also distally, possibly in transverse colon.
c. Why did the colonic fermentation increase following the ingestion of the
breakfast?
The movement of the colon is achieved by three different types of contractions
12
RESULTS AND DISCUSSION
(38): the individual phasic contractions that include the short- and long-duration
contraction, organized groups of contractions that include the migrating and
nonmigrating motor complexes, and special propulsive contractions (giant
migrating contractions). These contractions are controlled by myogenic, neural,
and chemical mechanisms. It has been shown that eating triggers ileal
evacuation and the movement of the colon(39;40). These eating triggered
movements might result in the transit of digesta and make the substrate available
for fermentation by pushing and stirring the digesta.
The study in appendix 1 showed that colonic fermentation factors were
related to the improved insulin sensitivity in peripheral tissues. We hypothesize
that SCFAs, as the main products of colonic fermentation non-digestible
carbohydrates are the possible mediators.
2. Relevance of SCFA production for insulin sensitivity
The association of SCFAs to insulin sensitivity, the possible mechanisms
involved and the possibility to prevent and control insulin resistance through
dietary manipulation of colonic fermentation were discussed in Appendix 2. The
colonic fermentation is a complicated process because fermentation rate, site
and pattern are all related to their physiological effects. Evidences exists that
SCFAs have insulin sensitizing effects. The underlying mechanisms include
suppression of pro-inflammation, improvement of FFA concentrations, and the
direct effect of SCFAs on glucose metabolism. The available data suggested that
the effect of SCFAs may depend on the SCFAs profile. Therefore, an optimal
SCFAs profile is needed for the colonic fermentation to have an insulin
sensitizing effect. Data from both in vivo and in vitro experiments showed that the
colonic production of SCFAs could be manipulated by using different substrates,
changing transit time, and applying enzyme inhibitors. It seemed that
manipulation of colonic fermentation could be an effective approach to prevent
insulin resistance and insulin resistance related diseases.
13
RESULTS AND DISCUSSION
The availability of the fermentation substrate is dependent mainly on the
amount and type of dietary carbohydrate. The caecum has the most abundant
substrate for fermentation due to its anatomic advantage. The caecum has also
the greatest number of microbiota. Therefore, fermentation is the most active in
caecum (41;42). SCFAs production and absorption in caecum are also higher
than that of the distal parts of the colon. It was believed that the production of the
SCFAs is the determinant of the plasma SCFAs concentrations because the
absorption capacity of the colon is far more than the colonic SCFAs
concentrations that can be reached physiologically (43).
The SCFAs profile is not only determined by the type of non-digestible
carbohydrate, but also the whole gut transit. The OCTT may change SCFAs
profile by influencing the availability of fermentation substrate, while the colonic
transit may determine the SCFAs by changing the fermentation site. Different
colon sites are dominated by different microbiota which may favor different
substrate and produce a different SCFA profile. The fermentation occurring in
colon beyond caecum may be important as colon cancer and ulcerative colitis
are mainly occurred in the distal colon. On the one hand, the fermentation will
determine the local SCFAs production which can influence the local colon health
and have a systemic effect after absorption. On the other hand, spatial change of
the fermentation site from proximal colon to distal colon also means temporal
change of SCFAs production which may influence the SCFAs profile in the blood
circulation. How much the host benefits from the colonic fermentation is therefore
influenced by various factors affecting the SCFAs profile. Current available
evidence may not be enough to define an optimal SCFAs profile. A higher total
SCFAs concentration with higher butyrate proportion, as seen with the plasma
profile of SCFAs derived from barley kernel (44), could be an optimal SCFAs
profile on the basis of metabolic effects.. The relation between the SCFAs profile
and insulin sensitivity should be further studied. Results from this type of study
may be not only important to the prevention of insulin resistance and its related
diseases, but also important towards other aspects of colonic fermentation, for
14
RESULTS AND DISCUSSION
example, extraction of energy (45).
3. Monitoring the ratio of digestion vs. fermentation of substrates
In order to be able to manipulate colonic fermentation, it is important to know the
fermentable part of the substrate. We developed a mathematical model to
evaluate the degree of digestion and fermentation for a carbohydrate food
(Appendix 3).
Boiled barley kernel containing 50 g available carbohydrate was given to 17
young volunteers. The breath samples were collected for 12 h to measure
hydrogen and
13
CO2. Four hours after intake of barley, breath hydrogen started
to increase significantly which was regarded as a sign of colonic fermentation.
This suggested that the digesta of the test meal reached the caecum and started
to ferment about 4 h after intake of the test meal.
13
CO2 (%dose/h) increased and reached peak values 4 h after intake of
The
the barley test meal. It decreased after the peak value, but was still higher than
the baseline value 12 h later. The barley was labeled with
13
C during growth
which implies that both the digestible part and non-digestible part of the barley
kernel were labeled and could produce
caecum, all the
13
CO2. Before the digesta reached the
13
CO2 came from the small intestinal digestion process, after that,
13
CO2 was also produced by colonic fermentation. With the
13
CO2 data derived
from the digestion process a best fit curve was constructed, which subsequently
could be used to estimate the total
13
CO2 derived from the digestion process.
The difference between the fitted curve and the observed curve reflected the
contribution of
13
CO2 from colonic fermentation. The results showed that 18-19%
of the recovered
13
CO2 was from colonic fermentation. The samples were only
collected for 12 h; the information about digestion and fermentation after 12 h
could be estimated using the fitted and observed curve. By doing this, we found
that the
25% of
13
CO2 levels would return to baseline after 24 h, and estimated that 24-
13
C was contributed by colonic fermentation.
15
RESULTS AND DISCUSSION
It was suggested the energy value of dietary fiber and RS is 2 kcal/g (46).
According to the content of dietary fiber and RS in the test meal, the energy
contribution can be estimated to be 14.8%. The barley was ingested as boiled
kernel. The relative intact physical and chemical structure made it possible that
more carbohydrate may escape the small intestinal digestion and reach the colon
for fermentation (30). In this sense, it’s reasonable that we derived a higher value
(24-25% vs. 14.8%).
Our results with the curve fitting model showed promising results which might
be applied with other foods as well. With
13
C labeled foods available, we may
obtain a specific fermentation index (FI) for each carbohydrate food or diet. The
fermentation index together with the GI would be able to provide comprehensive
information about the biological characteristics of carbohydrate rich foods.
4. SCFAs profile in serum and urine
A fermentation index would be able to reflect the fermentation characteristic in
general, but cannot provide information about the SCFAs profile. As was
discussed above, the SCFAs profile could be the critical determinant for its effect.
We therefore developed a method to monitor the SCFAs profile in plasma and
urine derived from colonic fermentation. The possibility of manipulating the
SCFAs by using different non-digestible carbohydrates sources was investigated
(Appendix 4).
After ingestion of barley kernel or barley porridge test meals which provide 50
g available carbohydrates, breath samples were collected for 6 h, blood samples
14 h, and urine for 24 h. The OCTT estimated from breath hydrogen and
14
CO2
was the same for both test meals, barley porridge (360 ± 47 min for H2measurements and 365 ± 55 min for
14
CO2-measurements) and barley kernels
(372 ± 18 min for H2-measurements and 400 ± 0 min for
14
CO2-measurements)
(P = 0.705 for H2-measurements and P = 0.285 for 14CO2-measurements).
An increase of
13
C-acetate was observed early after ingestion of the meals (<
16
RESULTS AND DISCUSSION
6 h) for both test meals. A rise in
13
C-propionate in the fermentation phase could
only be detected after the porridge and not after the kernels meal. An increase in
13
C-butyrate was found for both test meals in the fermentation phase and was
higher after the barley kernels. Urine
13
C-SCFAs data were consistent with these
observations.
This study showed that plasma and urine SCFAs profiles of different nondigestible carbohydrates can be monitored. The application of
13
C labeled
substrate made it possible that the observed SCFAs could be linked to the
specific substrate. This technique would help to indentify the causal relationship
between SCFAs and its effect, e.g. improvement of insulin sensitivity.
The study also provided evidence that colonic fermentation can be
manipulated by dietary carbohydrates. It was known that soluble fiber is
fermentable and completely used by colonic microbiota, whereas the insoluble
fiber is less fermentable and contributes to the bulk of the feces. But the
fermentation of soluble fiber may be affected by the presence of insoluble fiber
and RS (47). By selecting different substrates, or combining different substrates,
SCFAs profile of colonic fermentation can be manipulated.
Due to the limitation to access colonic fermentation, information about colonic
fermentation has not been widely available. Although it is critical for biological
studies on effects of colonic fermentation, the quantitative evaluation of
fermentation is still very difficult. Most of the studies concerning colonic
fermentation measured SCFAs profile in faeces. The SCFAs profile in faeces
may not be the same as that in caecum, and obviously not the same as in
plasma. On the one hand, it was shown that the SCFAs concentration decreased
in the order of caecum, ascending colon, transverse colon and rectum, although
the proportion of the individual SCFA may keep stable (41). On the other hand,
we don’t know whether the higher faeces SCFAs concentrations are the outcome
of higher production or lower absorption. Before we have information about this,
it would be reasonable to refer to the SCFAs of a known food for the optimal
SCFAs.
17
RESULTS AND DISCUSSION
Whole grain barley has been used in our studies and in the studies of others.
Based on these results (18;21-23;48), we hypothesize that a food or diet which
has a favorable whole-gut transit and produce a SCFAs profile similar to that of
barley kernel are potent in prevention of T2DM.
In conclusion, the
results in this thesis showed evidence that colonic
metabolism of fermentable carbohydrates was related to insulin sensitivity and
anti-inflammatory effects , which is considered as highly relevant for the
prevention of Type 2 Diabetes .This preventive effect might be dependent on the
quantity and quality of the SCFA profile. The quantity of the fermentation can be
monitored by the Fermentation Index, derived from a mathematical model
developed by us. Furthermore we were able to demonstrate that fermentation
and thereby the SCFAs profile can be manipulated by the choice of substrates.
Manipulating the colonic fermentation with dietary carbohydrates to produce the
optimal SCFAs profile could play an important role in prevention of chronic
diseases associated with decreased insulin sensitivity including T2DM and
cardiovascular diseases.
18
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44. Priebe MG, Wang H, Weening D, Schepers M, Preston T, Vonk RJ.
Factors related to colonic fermentation of non-digestible carbohydrates of a
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45. Isken F, Klaus S, Osterhoff M, Pfeiffer AF, Weickert MO. Effects of longterm soluble vs. insoluble dietary fiber intake on high-fat diet-induced
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47. Topping DL. Soluble fiber polysaccharides: Effects on plasma cholesterol
and colonic fermentation. NUTR REV 1991;49:195-203.
48. Nilsson AC, Ostman EM, Granfeldt Y, Bjorck IME. Effect of cereal test
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Nutr 2008;87:645-54.
23
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
APPENDIX 1
Colonic fermentation of non-digestible
carbohydrates of a previous evening meal
increases tissue glucose uptake and moderates
glucose-associated inflammation
Marion G. Priebe
Hongwei Wang
Desiree Weening
Marianne Schepers
Tom Preston
Roel J. Vonk
Adapted from: American Journal of Clinical Nutrition 2010;91:90-97.
30
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
Abstract
Background
Evening meals rich in non-digestible carbohydrates have been shown to lower
postprandial glucose concentrations after ingestion of high glycemic index
breakfasts. This phenomenon is linked to colonic fermentation of non-digestible
carbohydrates, but the underlying mechanism is not fully elucidated.
Objective
We examined in which way glucose kinetics and related factors are changed
after the breakfast due to colonic fermentation.
Design
In a cross-over design 10 healthy male subjects ingested as an evening meal
white wheat bread (WB) or cooked barley kernels (BA), rich in non-digestible
carbohydrates. In the morning after intake of 50 g
13
C-enriched glucose the dual
isotope technique was applied to determine glucose kinetics. Plasma insulin, free
fatty acid, interleukin 6 (IL 6), tumor necrosis factor-α (TNF-α), short-chain fatty
acid concentrations and breath hydrogen excretion were measured.
Results
The plasma glucose response after the glucose drink was 29 % lower after the
BA evening meal (P=0.019). The insulin response was the same, whereas tissue
glucose uptake was 30 % higher (20.2±1.9 vs 15.5±1.8 mL/2h mean±SEM
respectively, P=0.016) after the BA evening meal, demonstrating higher
31
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
peripheral insulin sensitivity (P=0.001). The 4-h mean postprandial IL 6 (19.7±5.1
vs 5.1±0.7 pg/mL, P=0.024) and TNF-α (7.8±2.1 vs 5.3±1.6 pg/mL, P=0.008)
concentrations after the glucose drink were higher after the WB evening meal.
Butyrate concentrations (P=0.041) as well as hydrogen excretion (P=0.005) were
higher in the morning after the BA evening meal.
Conclusions
In healthy subjects factors related to colonic fermentation of non-digestible
carbohydrates increase peripheral insulin sensitivity and moderate glucoseassociated inflammation.
Introduction
In observational studies whole grain foods and foods with low glycemic index
(GI) and rich in non-digestible carbohydrates have been associated with
decreased risk of type 2 diabetes (1) and cardiovascular disease (2). These
foods are thought to exert their beneficial effect by decreasing postprandial blood
glucose response by slowing gastric emptying and/or delaying starch digestion
and starch-derived glucose absorption. Besides this immediate postprandial
effect, those foods have also been shown to influence blood glucose response
following a subsequent meal. In several studies, evening meals rich in nondigestible carbohydrates have been demonstrated to have the ability to reduce
postprandial glucose to a high GI breakfast (3-5).This overnight second meal
32
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
effect is purported to be due to short-chain fatty acids (SCFA; acetate,
propionate, butyrate) produced by fermentation of non-digestible carbohydrates
by the colonic microbiota, even though other yet unknown mechanisms can not
be excluded. These SCFA are rapidly absorbed from the colonic lumen and
metabolized by colonic epithelial cells, but part of them also enter the portal and
peripheral circulation (6). Effects of SCFA on liver metabolism have been
reported (7) and implicated with effects on the adipose tissue metabolism (8) and
secretome (9). Although the liver extracts approximately 75 % of acetate, 90 % of
propionate and 95 % of butyrate from the portal vein (10) higher concentrations
of SCFA in the peripheral circulation have been observed after ingestion of nondigestible carbohydrates (8;11). Several hypotheses as to how SCFA mediate
the glucose lowering effect have been postulated. SCFA may delay gastric
emptying (12), have insulin-like properties (8), increase insulin sensitivity by
decreasing free fatty acid concentrations (13), have anti-inflammatory effects
(14) or promote insulin independent glucose sparing.
Until now, mainly total blood glucose concentrations are reported after the
high GI breakfast. This makes it difficult to examine in detail underlying
mechanisms of the second meal effect, because total blood glucose is the net
result of influx of exogenous glucose from the small intestine into the systemic
circulation, endogenous glucose production and uptake of glucose into
peripheral tissue. Therefore, we conducted a study to investigate the underlying
glucose kinetics using the dual isotope technique. In addition, several factors
33
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
related to the regulation of glucose homeostasis, like free fatty acid
concentrations, inflammation markers and adiponectin as well as plasma
concentrations of SCFA were measured.
Subjects and methods
Experimental design
The study was performed in a randomized crossover manner, with each subject
studied on two occasions one week apart. The order of which the evening meal
was administered first was determined by the order of recruitment. The subjects
were asked to refrain from consuming foods enriched in 13C, such as cane sugar,
corn, corn products and pineapple, for the three days preceding the experiments
and from alcohol and strenuous exercise for 24 h before each study day. Each
subject recorded his food consumption on the first study day and consumed the
same food on the second study day. On both study days, the subjects were
present at the clinical research unit from 21.30 h the evening before the
experiment. The evening meal was taken at 22.00 h and the subjects stayed
overnight. In this way food intake could be controlled and physical activity
reduced. After the evening meal the subjects were fasting until the oral glucose
tolerance test (OGTT), which started at 8.00 h in the morning. Catheters were
inserted into veins in both forearms, one for collection of blood sample, kept
patent with heparin (50 IE/mL) and the other for infusion of D-[6,6-2H2] glucose
34
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
(98% 2H APE) (Isotec Inc, Miamisburg, OH, USA). At the start of the experiment
at 6.00 h (t = -120 min) one bolus of D-[6,6-2H2]glucose solution (80 times the
amount infused per minute) was infused and thereafter the continuous infusion of
0.07 mg/min/kg bodyweight D-[6,6-2H2]glucose solution started. Two hours after
the start of the continuous infusion (t = 0) the OGTT was started with 55 g
glucose (90 % carbohydrates) (glucose-monohydrate, Natufood, Natuproducts
BV, Harderwijk, The Netherlands) dissolved in 250 mL of drinkable tap water.
The glucose used was corn derived and therefore naturally labeled with
which is necessary to be able to apply the dual isotope technique. The
abundance of glucose was 1.09837 atom %
13
C,
13
C
13
C. Throughout the study, subjects
relaxed by reading or watching videos, to restrict physical activity.
Subjects
Ten healthy Caucasian male subjects [age 21 ± 2.0 y (mean  SEM), body mass
index 21.4 ± 1.0 kg/m2] were recruited in Groningen, The Netherlands, by
advertising. Start of recruitment was October 2006. The criteria for exclusion
were use of medications, blood donation in the previous 6 months, use of
antibiotics in the last 3 months, gastrointestinal symptoms, diabetes mellitus and
gastrointestinal surgery. Approval was obtained from the Medical Ethics
Committee of the University Medical Centre in Groningen and each subject gave
written informed consent for the study.
Evening meals
The evening meals were either 105 g of white wheat bread (WB) or 86 g (dry
35
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
weight) of cooked whole barley kernels (BA). WB (Zaanse Snijder wit) was
obtained at a local supermarket (Albert Hein, Groningen, The Netherlands). BA
(Whole grain brown barley, Nature’s Harvest, Burton-upon-Trent, UK) was
cooked in 250 g water with 1 g of salt for 32 min until all the water was absorbed.
Each test meal provided 50 g available carbohydrate and was taken with 250 mL
of drinkable tap water. It was estimated that the WB and BA contained 2 g and
15 g non-digestible carbohydrates, respectively, as the degree of pearling of BA
was the same as in the study of Nilsson et al (15).
Sample collection
Blood was collected throughout the experiment into tubes containing sodium
fluoride/potassium oxalate. After centrifugation (1000 x g; 10 min) at 4 oC the
samples were stored at –20 oC until assayed. Breath samples were collected by
breathing
through
a
straw
into
10-mL
Exetainers
(Labco
limited,
Buckinghamshire, United Kingdom). Before ingestion of the breakfast 6 basal
breath samples (-120, -90, -60, -30, -15, 0 min) and 5 basal blood samples (-90, 60, -30, -15, 0 min) were taken. The sample of time point 0 was collected directly
before the start of the OGTT. During the OGTT blood samples were taken every
15 min for 2 h and every 30 min for additional 2 h. After taking a blood sample
0.6 mL heparin (50 E/mL) was injected into the catheter to prevent blocking of
the tube by clotting. Before taking the next blood sample the heparin was
withdrawn with a small amount of blood and discarded.
Analytical procedures
36
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
Glucose was measured with an ECA-180 glucose analyzer (Medingen, Dresden,
Germany). The inter-assay and intra-assay coefficient of variation was 3 % and 1
%, respectively. Insulin concentrations were measured in duplicate using a
commercially available radioimmunoassay (Diagnostic Systems Laboratories,
Webster, Texas, USA). The inter-assay and intra-assay coefficient of variation
was 9.9 % and 4.5 %, respectively. The derivatization of plasma glucose to
glucose penta-acetate for the analysis of the isotopic enrichment of plasma
glucose is described in detail elsewhere (16). The
13
C/12C isotope ratio
measurement of the glucose penta-acetate derivative was determined by Gas
Chromatography/Combustion/Isotope Ratio Mass Spectrometry (GC/C/IRMS)
(TracerMAT, Thermo Finnigan, Bremen, Germany) and the 2H enrichment was
measured
by
Gas
Chromatography/Mass
Spectrometry
(GC/MS) under
conditions previously described (17;18). SCFAs were measured by GC/MS as
described by Morrison et al (19). All plasma samples of one subject were
analyzed in one batch to eliminate the effects of inter-batch variation. Breath
hydrogen analysis was performed by GC (HP 6890 Agilent, Hewlett Packard Co,
Palo Alto, USA), using a CP-Molsieve 5A column of 25 m x 0.53 mm (50 μm film
thickness) (Chrompack International B.V., Bergen op Zoom, The Netherlands).
Plasma FFA levels were determined with an enzymatic colorimetric method
(NEFAC ACS-ACOD Method, Wako Chemicals, Richmond, VA, USA); samples
were read at 540 nm on a BioTek EL 800 microplate reader (BioTek, Winooski,
VT, USA). Plasma IL 6 and TNF-α were determined using the luminex Bio-plex
37
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
Cytokine Assay (Bio-Rad laboratories, Hercules, CA, USA); adiponectin was
determined using the luminex Bio-Plex Pro Assay Diabetes (Bio-Rad
laboratories, Hercules, CA, USA); samples were read on the Luminex 100 Total
System (Luminex, Austin, TX, USA).
Calculations
Glucose kinetics
The molar percent enrichment of [6,6-2H2] glucose and the
13
C atom percentage
were calculated as previously described (17) and smoothed using the Optimized
Optimal Segments (OOPSEG) program developed by Bradley et al (20). The rate
at which total glucose appeared in plasma (RaT) from exogenous (meal) and
endogenous (hepatic) sources was calculated using the non-steady state
equation of Steele (21) as modified by De Bodo (22). Identical behavior of
labeled and unlabelled glucose molecules was assumed. The effective volume of
distribution was assumed to be 200 mL/kg and the pool fraction value 0.75 (23).
The systemic rate of appearance of exogenous glucose (RaE) was estimated as
described by Tissot et al
(23) and endogenous glucose production (EGP)
calculated by subtracting RaE from RaT (23). The glucose clearance rate (GCR)
reflecting the insulin-mediated glucose uptake of tissue, was calculated as
described by Schenk et al (24).
Incremental areas under the curves
In order to determine differences in glucose kinetics as well as in plasma
38
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
glucose, insulin, FFA and SCFA concentrations the incremental areas under the
curves (iAUC) of 0 - 2 h and 0 - 4 h were calculated and compared. For these
measurements also peak (nadir) concentrations or rates and time to peak (nadir)
were compared. The time to peak (nadir) was defined as the time period
between the intake of the glucose drink and the appearance of peak (nadir)
plasma concentrations or rate. For the iAUC calculations the value of the fasting
measurements was taken as the baseline value. Areas below baseline were not
included. For the iAUC calculations of GCR, RaT and RaE, the values were
multiplied by bodyweight. The iAUC of RaE was expressed as percentage of the
administered dose of glucose equivalents (cum dose %). The EGP and FFA
concentrations were suppressed after the oral glucose tolerance test. Therefore,
the data were symmetrically transformed with the baseline as the symmetric axis
to calculate the iAUC. For the other measurements which did not show any
obvious rise in concentration on one occasion (breath hydrogen and plasma
cytokines) the mean fasting and mean 0 - 2 h and 0 - 4 h postprandial values
were calculated and compared.
Insulin sensitivity
An index for peripheral insulin sensitivity (SI) for use after OGTT was calculated
based on the method developed by Cederholm et al (25) with some
modifications. SI was expressed as the ratio of metabolic glucose clearance rate
(MCR) to log mean postprandial plasma insulin. We calculated MCR as 0 - 2 h-
39
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
iAUC of GCR (including areas below baseline) multiplied by body weight and
divided by 1000. Cederholm et al (25) derived the MCR from plasma glucose
measurements during the OGTT and estimates of the glucose space.
Statistical analyses
Data are presented as mean ± SEM. The univariate procedure of the General
Linear Model was applied to test statistical differences, with test meal and
sequence of the evening meal as fixed factor and subject as random factor. All
analyses were performed with SPSS 14.0 for Windows (release 14.0.2, SPSS
Inc, Chicago, USA). A value of P < 0.05 was considered statistically significant.
Results
Plasma glucose and insulin
The results are summarized in Table 1. In the morning after the BA evening meal
the 0 - 2 h glucose iAUC during the OGTT was 29% smaller (P = 0.019) than that
after the WB evening meal. Also, the 0 - 4 h iAUC and peak concentrations
(Figure 1 A) were significantly different (P = 0.014 and P = 0.041 respectively).
However, no differences were found between the insulin responses after both
evening meals for any of the indices compared (Table 1, Figure 1 B).
40
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
Figure 1 Mean ( SEM) of incremental plasma glucose changes (delta) (A) and
of plasma insulin concentrations (B) after a 50 g oral glucose tolerance test in the
morning after a barley kernel (BA) or white bread (WB) evening meal. The 0 - 2 h
and 0 - 4 h glucose iAUC were lower (P = 0.019 and P = 0.014, respectively)
after the BA evening meal whereas that of insulin were the same (Univariate
General Linear Model). N = 10 healthy male subjects.
A
delta Glucose (mmol/L)
5
barley
4
bread
3
2
1
0
-1
0
30
60
90
120
150
180
210
240
-2
Time (min)
B
450
barley
Insulin (mU/L)
350
bread
250
150
50
-50 0
30
60
90
120
150
Time (min)
41
180
210
240
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
Figure 2
Mean ( SEM) of the glucose clearance rate (GCR) after a 50 g oral
glucose tolerance test in the morning after a barley kernel (BA) or white bread
(WB) evening meal. The 0 - 2 h iAUC of GCR was significantly higher (P =
0.016) after the BA evening meal. (Univariate General Linear Model). N = 10
GCR (mL.kg-1min-1)
healthy male subjects.
8
Barley
7
bread
6
5
4
3
2
1
0
0
30
60
90
120
150
180
210
240
Time (min)
Blood glucose kinetics
The results are summarized in Table 1. RaT, RaE and EGP did not differ in any
of the compared indices in the morning after the BA and WB evening meals. In
the morning after the BA evening meal the 0 - 2 h iAUC of GCR was 30 %
greater (P = 0.016) than after the WB evening meal. The GCR reached a peak
value earlier after the BA than after the WB evening meal (P = 0.011) but the
peak values were the same (P = 0.059) (Table 1, Figure 2).
Plasma FFA
42
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
No differences were found in any of the compared indices for FFA concentrations
(Table 1).
Plasma SCFA
Propionate, acetate and total SCFA plasma concentrations did not differ in any
indices compared (Table 1). However, the 0 - 2 h iAUC of butyrate was
significantly higher in the morning after the BA evening meal compared with the
WA evening meal (P = 0.041) (Figure 3).
Breath hydrogen
The mean 0 - 2 h and 0 - 4 h concentrations of hydrogen during the OGTT in the
morning after the BA evening meal were significantly higher than that after the
WB evening meal (P = 0.005 for both) (Table 2, Figure 4).
Plasma cytokines
The results are summarized in Table 2. Mean postprandial 0 - 4 h concentrations
of IL 6 (Figure 5 A) and TNF-α (Figure 5 B) were significantly higher in the
morning after the WB evening meal than after the BA evening meal (P = 0.024
and P = 0.008, respectively), however, mean postprandial 0 - 2 h concentrations
not (P = 0.079 and P = 0.054, respectively). No significant differences were
observed for adiponectin. (Table 2).
Insulin sensitivity index
Insulin sensitivity was significantly impaired in the morning after the WB evening
meal compared with that following the BA evening meal (BA, 30.1 ± 1.9; WB,
24.5 ± 1.5, P = 0.001).
43
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
Figure 3 Mean ( SEM) of plasma butyrate concentrations before and after a 50
g oral glucose tolerance test in the morning after a barley kernel (BA) or white
bread (WB) evening meal. The 0 - 2 h iAUC was significantly higher (P = 0.041)
after the BA evening meal whereas the 0 - 4 h iAUC were the same after both
evening meals (Univariate General Linear Model). N = 10 healthy male subjects.
3.5
barley
Butyrate (µmol/L)
3.0
bread
2.5
2.0
1.5
1.0
0.5
-90 -60
0.0
-30 0
30
60
90
120 150 180 210 240
Time (min)
44
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
Figure 4 Mean ( SEM) of breath hydrogen concentrations before and after a 50
g oral glucose tolerance test in the morning after a barley kernel (BA) or white
bread (WB) evening meal. The breath hydrogen concentrations (0 - 2 h and 0 - 4
h postprandial) were significantly higher (P = 0.005 both) after the BA evening
meal. (Univariate General Linear Model). N = 10 healthy male subjects.
80
barley
70
bread
Hydrogen (ppm)
60
50
40
30
20
10
0
-120
-60
0
60
120
Time (min)
45
180
240
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
Figure 5
Mean ( SEM) of plasma IL 6 concentrations (A) and TNF-α
concentrations (B) before and after a 50 g oral glucose tolerance test in the
morning after a barley kernel (BA) or white bread (WB) evening meal. The 0 - 2 h
mean postprandial IL 6 and TNF-α concentrations were the same after both
evening meals whereas the mean 0 - 4 h mean postprandial IL 6 and TNF-α
concentrations were lower (P = 0.024 and P = 0.008 respectively) after the BA
evening meal (Univariate General Linear Model). N = 10 healthy male subjects.
A
barley
IL- 6 (pg/mL)
50
45
40
35
30
25
20
15
10
5
0
-60
-30
bread
0
30
60
90
120
Time (min)
46
150
180 210
240
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
B
18
16
barley
bread
TNF-α (pg/mL)
14
12
10
8
6
4
2
0
-60
-30
0
30
60
90
120
Time (min)
47
150 180
210 240
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
Table 1 Indices reflecting the response of plasma glucose, insulin, free fatty acids (FFA), short chain fatty acids (SCFA),
the glucose clearance rate (GCR), systemic rate of appearance of total glucose (RaT), rate of appearance of exogenous
glucose (RaE) and endogenous glucose production (EGP) to a 50 g oral glucose tolerance test in the morning after a
barley kernel (BA) or white bread (WB) evening meal. N = 10 healthy male subjects.
Glucose (mmol/L)
Insulin (mU/L)
RaT4 (mg·kg-1·min-1)
RaE5 (mg·kg-1·min-1)
Fasting Values
Time to peak (min)
Peak values
iAUC1 0 - 2 h
iAUC 0 - 4 h
BA2
5.1 ± 0.1
40.5 ± 3.2
8.5 ± 0.3a
167.1 ± 18.9a
170.2 ± 21.1a
WB3
5.0 ± 0.1
39.0 ± 2.5
9.1 ± 0.4
234.9 ± 29.9
241.4 ± 30.9
BA
6.3 ± 0.7
40.5 ± 3.9
72.5 ± 11.4
3035 ± 417
3121 ± 431
WB
5.5 ± 0.6
43.5 ± 3.5
70.1 ± 6.5
3753 ± 392
4057 ± 481
BA
2.5 ± 0.1
54.0 ± 3.3
7.1 ± 0.2
27.0 ± 1.2
35.7 ± 1.5
WB
2.5 ± 0.1
51.0 ± 3.3
6.6 ± 0.4
24.7 ± 1.5
33.0 ± 1.7
BA
0
60.0 ± 5.5
5.3 ± 0.2
66.2 ± 2.4
89.7 ± 2.4
WB
0
57.0 ± 7.7
4.8 ± 0.4
61.5 ± 3.1
86.7 ± 1.7
Fasting Values
Time to peak (min)
Peak values
iAUC1 0 - 2 h
iAUC 0 - 4 h
48
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
EGP6 (mg·kg-1·min-1)
GCR7 (mL·kg-1·min-1)
FFA8 (mmol/L)
Acetate (µmol/L)
Propionate (µmol/L)
Butyrate (µmol/L)
Total SCFA9 (µmol/L)
1
BA
2.5 ± 0.1
90.0 ± 10.5
1.5 ± 0.1
86.3 ± 9.4
142.0 ± 22.7
WB
2.5 ± 0.1
99.0 ± 10.1
1.4 ± 0.1
85.5 ± 9.3
148.1 ± 25.0
BA
2.8 ± 0.1
84.0 ± 7.1a
8.1 ± 0.5
20.2 ± 1.9a
33.4 ± 2.6
WB
2.8 ± 0.1
118.5 ± 9.1
7.0 ± 0.3
15.5 ± 1.8
29.0 ± 2.1
BA
0.39 ± 0.04
94.5 ± 3.2
0.03 ± 0.01
28.6 ± 3.7
43.9 ± 6.7
WB
0.48 ± 0.04
103.5 ± 6.9
0.04 ± 0.01
36.3 ± 3.7
57.6 ± 6.6
BA
479.7 ± 61.2
36.0 ± 6.0
864.4 ± 207.3
11934 ± 4601
13480 ± 5259
WB
418.6 ± 47.8
78.0 ± 19.6
732.3 ± 117.1
15389 ± 5942
19782 ± 7296
BA
10.3 ± 0.6
51.0 ± 11.9
16.0 ± 1.5
223.8 ± 41.9
258.3 ± 44.9
WB
9.9 ± 0.7
36.0 ± 4.0
15.3 ± 1.3
245.5 ± 39.8
264.7 ± 42.9
BA
1.8 ± 0.2
60.0 ± 23.7
2.9 ± 0.3
48.6 ± 10.1a
74.7 ± 17.7
WB
1.6 ± 0.1
84.0 ± 35.2
2.6 ± 0.2
23.9 ± 6.2
42.4 ± 11.4
BA
491.8 ± 61.5
36.0 ± 6.0
882.7 ± 207.3
12330 ± 4582
13883 ± 5267
WB
430.1 ± 48.1
78.0 ± 19.6
747.4 ± 117.3
15615 ± 5969
20007 ± 7311
Units of the 0 - 2 h and 0 - 4 h iAUC for glucose, insulin, FFA, acetate, propionate, butyrate and total SCFA are the unit of
49
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
the corresponding parameter per 2 h or 4 h respectively; units of 0 - 2 h and 0 - 4 h iAUC of RaT and EGP are g per 2 h
and g per 4 h, respectively; units of 0 - 2 h and 0 - 4 h iAUC of GCR is mL per 2 h and mL per 4 h; units of 0 - 2 h and 0 - 4
h iAUC of RaE are %dose/2 h and %dose/4 h, respectively.
2
barley kernel evening meal;
3
white bread evening meal;
appearance of exogenous glucose;
6
4
systemic rate of appearance of total glucose;
endogenous glucose production;
7
free fatty acids;
chain fatty acids.
a
significantly different from the WB result, P < 0.05 (Univariate General Linear Model).
50
8
5
glucose clearance rate;
rate of
9
short
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
Table 2 Mean ( SEM) concentrations of breath hydrogen, plasma IL 6, TNF-α,
and adiponectin concentrations before and after a 50 g oral glucose tolerance
test in the morning after a barley kernel (BA) or white bread (WB) evening meal.
N = 10 healthy male subjects.
Mean 0 - 2 h
Mean 0 - 4 h
postprandial
postprandial
concentration
concentration
Fasting
concentration
BA1
31.5 ± 3.4
40.0 ± 6.5a
39.5 ± 7.0a
WB2
24.4 ± 3.5
15.2 ± 1.4
14.2 ± 1.2
BA
7.0 ± 0.8
5.2 ± 0.8
5.1 ± 0.7a
WB
13.1 ± 4.2
15.0 ± 4.5
19.7 ± 5.1
BA
5.7 ± 1.9
5.5 ± 1.8
5.3 ± 1.6a
WB
6.6 ± 1.8
6.7 ± 1.7
7.8 ± 2.1
Adiponectin
BA
27.0 ± 5.4
26.5 ± 4.4
28.3 ± 4.5
(µg/mL)
WB
32.0 ± 5.8
30.4 ± 4.5
30.6 ± 4.8
Hydrogen (ppm)
IL 63 (pg/mL)
TNF-α4 (pg/mL)
1
barley kernel evening meal; 2 white bread evening meal;
3
interleukin 6;
4
tumor
necrosis factor - α;
a
significantly different from the WB result, P < 0.05 (Univariate General Linear
Model).
51
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
Discussion
The findings of this study elucidate in which way glucose kinetics after a high GI
breakfast are altered due to the ingestion of an evening meal rich in nondigestible carbohydrates and show that a glucose-associated rise in proinflammatory cytokines can be moderated by the previous evening meal.
With the dual isotope technique we were able to show that reduced glucose
response to a 50 g OGTT after a BA evening meal was due to higher glucose
uptake into peripheral tissue, reflected by the GCR. It also excludes possible
effects mediated by SCFA on gastric emptying as proposed previously (12),
because the RaE was not different. In addition, modulation of EGP does not
seem to contribute to the reduced glucose response.
So far, only one study investigated postprandial glucose kinetics after the
breakfast in an overnight second meal study design with the dual isotope
technique (26). In this study no change in GCR was found after 75 g glucose in
the morning after an evening meal of BA as compared with brown rice. However,
these data were only reported for one postprandial time point (60 min), which
makes comparison with our results difficult. In the study of Thornburn et al (26)
lower EGP was observed after the BA evening meal. The difference in EGP was
mainly due to an initial pronounced increase of EGP 20 min after the start of the
OGTT which occurred after the rice but not the BA evening meal. In our study, as
well as in other studies using the same technique to determine EGP (27;28), an
52
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
initial increase in EGP was not observed. The differences in results could be
either due to the characteristics of the subjects or the characteristics of the
evening meals low in non-digestible carbohydrates.
The higher GCR in our study was not accompanied by higher insulin
concentrations, implying that tissue sensitivity to insulin has been higher after the
BA evening meal. EGP was the same after both evening meals, which suggests
that only peripheral and not hepatic insulin sensitivity was altered. We tested this
assumption by calculating an index of peripheral insulin sensitivity (25) by using
GCR, which was indeed significantly different. Other overnight second meal
studies, so far, have not reported insulin sensitivity. However, the results of one
study with a similar design are consistent with our results. Higher insulin
sensitivity (calculated with the minimal model) in healthy subjects was found after
a standard breakfast after administering three high resistant starch meals on the
previous day compared with meals without resistant starch (29). Our findings are
also in line with results of more longer term interventions, as supplementation of
a diet with either cereal fiber enriched bread for 3 days (30) or 30 g resistant
starch per day for 4 weeks (8) increased insulin sensitivity (measured with the
euglycemic hyperinsulinemic clamp) in healthy subjects. The results of our study
thus indicate that food associated factors acutely can influence peripheral insulin
sensitivity. This is the more remarkable as the observation is made in healthy
lean volunteers, in whom glucose homeostasis can be expected to be optimally
regulated.
53
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
Breath hydrogen was higher in the morning after the BA evening meal,
supporting the hypothesis of involvement of colonic fermentation in the overnight
second meal effect. Furthermore, we observed 103 % higher butyrate
concentrations in the morning after the BA evening meal, which is consistent with
results of Nilsson et al (4) but not in agreement with the findings of two other
studies (15;29). These inconsistencies in results could be due to differences in
time points of sampling, characteristics of the evening meal or analytical
methods. The study of Gao et al (31) gives an indication as to the possible
underlying mechanisms of the insulin sensitizing effect of butyrate. In that study
oral supplementation of butyrate (5 g/kg/day), which increased butyrate
concentrations in serum in fed condition by 71 %, prevented development of
insulin resistance and obesity in C57BL/6J mice on a high-fat diet. Cell culture
experiments to explore the underlying molecular mechanisms of their
observation showed the potency of butyrate to directly activate among others
adenosine 5’-monophosphate activated protein kinase (AMPK). The activation of
AMPK by pharmacological means previously has been shown to increase
glucose transport and cell-surface glucose transporter 4 (GLUT 4) content in
skeletal muscle from nondiabetic men (32). Thus, the results of our study
suggest that butyrate, derived from colonic fermentation of non-digestible
carbohydrates, could be involved in the overnight second meal effect. To prove a
causal relationship, however, intervention studies with butyrate are necessary.
Another novel finding of our study concerns the ability of an evening meal of
BA to prevent the late postprandial rise in the pro-inflammatory cytokines IL 6
54
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
and TNF-α after the glucose load in the morning. Recently, much attention has
been paid to inflammatory processes associated with acute hyperglycemia in
relationship to insulin sensitivity and atherosclerotic processes. Hyperglycemia
induced by a hyperglycemic clamp, oral glucose or high GI-meal has been
shown to increase nuclear factor kappa B activity (NF-κB) in peripheral blood
mononuclear cells (PBMC) in healthy lean volunteers (33-35). Nuclear factor
kappa B (NF- κB) plays a central role in inflammatory responses and is involved
in transcriptional regulation of many cytokines (36). In vitro, high glucose
concentrations induce toll-like receptor 2 and 4 expression in human monocytes
which leads to higher NF-kB activity and IL 6 and TNF-α secretion (37). Higher
plasma concentrations of IL 6 and TNF-α were observed after plasma glucose
concentrations have been acutely raised in a glucose clamp study in healthy
volunteers (38) as well as in a hyperinsulinemic euglycemic clamp study (39). In
addition IL 6 was higher postprandially after a high GI meal with no change in
TNF-α (40). It may be of interest to note, that similar activation of NF- κB or
increase in IL 6 and TNF-α have also been observed after fat containing meals
(40-42). The high glucose or meal-induced inflammatory response could be
prevented by concomitant intake of antioxidants (glutathione, vitamine C) (34;38)
or antioxidant rich food (olive oil, red wine) (41;43). Our observation that an
evening meal rich in non-digestible carbohydrates can also prevent the mealassociated rise in IL 6 the following morning is in line with the results from
Nilsson et al (44), however the effect of a previous meal on the rise of TNF-α
after a subsequent meal has not been examined before. Thus, our finding
55
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
suggests that not only concomitant intake of antioxidants but also so far
unknown antioxidant or anti-inflammatory mechanisms or factors derived from a
previous evening meal can moderate meal-associate inflammation.
What governs the factors responsible for suppression of the inflammatory
response - we only can speculate. On the one hand, this could be due to antiinflammatory properties of SCFA. Butyrate has been shown in vitro to decrease
pro-inflammatory cytokine expression via inhibition of NF- κB activation in PBMC
(45) and all three SCFA decreased lipopolysacharide-stimulated TNF-α release
from human neutrophils (46). On the other hand, also the presence of bioactive
compounds with anti-oxidative capacity could play a role in the observed effect.
Many classes of phenolic compounds are present in BA which have been shown
to have strong anti-oxidative capacity in vitro (47). Many of these bioactive
components are bound to cell walls of the grain and reach the colon where they
are released during the fermentation process (48). Currently, however, little
information is available about their antioxidant effects in vivo.
In summary, the results of our study highlight the potency of an evening meal
rich in non-digestible carbohydrates to increase tissue glucose uptake the next
morning, and suggest a possible role for butyrate or butyrate associated factors.
The observation of the late anti-inflammatory effect of BA is valuable with regard
to the exploration of new strategies to prevent high glucose or meal-induced
postprandial inflammation.
56
APPENDIX 1. COLONIC FERMENTATION AND INSULIN SENSITIVITY
Acknowledgements
We thank Elles Jonkers and Theo Boer for analysing
13
C- and 2H-glucose by
GC/MS and GC/C/IRMS and Karloes Landman for her assistance by conducting
the experiments.
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63
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
APPENDIX 2
Manipulation of colonic short-chain fatty acid
production: relevance for the prevention of
insulin resistance
Hongwei Wang
Roel J. Vonk
Marion G. Priebe
In process.
64
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
Abstract
Short-chain fatty acids (SCFAs) produced by colonic fermentation of nondigestible carbohydrates have been shown to have an impact on the host in
relation to the prevention of chronic diseases. Recent studies indicate that
SCFAs might be able to improve insulin sensitivity in humans. The colonic SCFA
production is affected by various factors including the presence of fermentable
substrate, the chemical and physical properties of the substrate, the composition
of the colon microbiota and the colon transit time. It has been suggested that the
amount and the proportion of SCFAs (SCFA profile) are determinants of their
physiological effect. We reviewed the evidence for a relationship between SCFA
production and insulin sensitivity, which has impact on various chronic diseases
and aging. The evidence showed that the insulin sensitizing effect of SCFAs
depends on the specific SCFA profile and this profile is influenced by various
factors related to colonic fermentation. We hypothesize that manipulating colonic
fermentation to produce an optimal SCFA profile is a promising tool to prevent
insulin resistance. The approaches to manipulate colonic fermentation, and the
potential of whole grain food in this regard are discussed.
Introduction
The colon, which was once simply regarded as an organ of fluid retention, has
been more and more recognized as an important metabolic organ. Colonic
fermentation is a complicated process by which among others short-chain fatty
acids (SCFAs), branch-chain fatty acids, CO2, CH4 and H2 (1) are produced.
Important substrates for colonic fermentation are dietary carbohydrates that
escape small intestinal digestion. They are (partly) fermented by colonic
microbiota to produce SCFAs (mainly acetate, propionate and butyrate).
65
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
Consumption of non-digestible carbohydrates has been shown to exert
beneficial health effects. Several studies have been conducted to investigate the
relationship between non-digestible carbohydrates and glucose tolerance or
insulin sensitivity. In the overnight second meal effect studies, the observation of
improved glucose tolerance after breakfast in the morning after an evening meal
rich in non-digestible carbohydrates indicated that colonic fermentation is likely to
be involved in this beneficial effect (2-6;7). Improved insulin sensitivity of
peripheral tissue was suggested as a mechanism for the second meal effect, and
SCFAs from colonic fermentation were suggested to play a role in this insulin
sensitizing effect (7).
The amount and proportion of SCFAs produced (the SCFA profile) are
determined by the type and amount of the substrate, the fermentation site, the
rate of fermentation and absorption as well as the composition and activity of the
colonic microbiota (8;9). All these aspects are affecting the fermentation process
and thereby the SCFA profile. This suggests that manipulation of colonic
fermentation to produce an optimal SCFAs profile may be a promising approach
to prevent insulin resistance and related diseases.
In this paper, the relevance of colonic fermentation for the prevention of
insulin resistance and the possibility of manipulating colonic fermentation through
dietary carbohydrates to obtain this beneficial effect were explored.
The relationship between plasma SCFA concentrations
and glucose tolerance or insulin sensitivity
In several second meal effect studies improved glucose tolerance was observed
in the morning after evening meals rich in non-digestible carbohydrates (2-6;1012). In addition, improved postprandial insulin sensitivity was observed in the
morning after a barley kernel evening meal (compared with white bread) (7) and
after consumption of four portions of resistant starch (RS) on the previous day
66
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
(compared with no RS-supplementation) (12). Higher concentrations of hydrogen
in breath, an indicator of colonic fermentation of non-digestible carbohydrates,
have been consistently observed in the morning after the evening meals rich in
non-digestible carbohydrates, suggesting an involvement of colonic fermentation
in the observed effect. Plasma concentration of SCFAs, however were only
measured in four studies (3;6;7;12). Two of these studies (6;7)reported higher
butyrate and another (3) higher propionate concentrations in the morning after
meals rich in non-digestible carbohydrates, whereas one study did not find any
difference compared with the control meal (12).
The association between plasma SCFA concentrations and insulin sensitivity
was also observed in an intervention study (12) which supplemented 30 g/d of
RS to healthy subjects. Four weeks later, insulin sensitivity (euglycemichyperinsulinemic clamp) was improved and plasma acetate and propionate were
higher after RS supplementation, compared with the control intervention (no RS).
The inconsistencies in SCFA results could be due to differences in time points of
sampling and characteristics of the evening meal. In addition, differences in
analytical methods may play a role as concentrations in the peripheral circulation
are in the micromolar range and require very sensitive analytical techniques.
Efforts have also been made to investigate direct effects of SCFAs on
glucose tolerance and insulin sensitivity by administering them orally or rectally.
Venter et al (13) showed that glucose tolerance of healthy young women was
improved after taking 7.5 g/day sodium propionate for 7 wk. Furthermore, it was
found that fasting glucose concentrations were decreased in obese male Zucker
(fa/fa) rats which were fed orally (1 g/d) or infused rectally (0.15 g/d) with
propionate for 19 d (14). In a recent study, butyrate was found to be able to
improve insulin sensitivity (Homeostasis model assessment for insulin resistance)
in high fat diet raised mice (15). In that study, oral supplementation of butyrate
(5% wt/wt in diet) prevented development of insulin resistance and obesity in
C57BL/6J mice on a high-fat diet. The insulin sensitizing effect of butyrate was
further supported by the results of an in vitro experiment which showed that
67
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
butyrate activated adenosine 5’-monophosphate activated protein kinase (AMPK)
which is related to improved insulin sensitivity (16). In another study, Boillot et al
(17) administered propionate (78 g/kg diet) to Sprague-Dawley rats for 3 wk. It
was found that, compared with control rats, plasma glucose was lower whereas
plasma insulin was the same for rats fed on diet supplemented with propionate.
However, in this study, whole insulin sensitivity was not changed as measured
with the euglycemic-hyperinsulinemic clamp. Taken together, there are
indications that SCFAs can influence insulin sensitivity. The mechanisms remain
largely unknown, but some hypotheses have been postulated.
Possible mechanisms by which SCFAs might influence
insulin sensitivity
1. SCFAs and cytokines
IL 6 and TNF-α are pro-inflammatory cytokines which have been related to the
development of insulin resistance and type 2 diabetes (18), whereas adiponectin
and leptin are regarded as protective cytokines (19;20). SCFAs produced by
colonic fermentation can have a direct effect on epithelial cells of the large
intestine. After being absorbed, SCFAs are also available for other tissues and
organs. Receptors for SCFAs, the orphan G protein-coupled receptors GPR41
and GPR43, have been found in adipose tissue, colon and immune cells (21-23).
Cytokines can be secreted by various cells and SCFAs have been shown in
several in vitro studies to be capable of regulating their secretion. Butyrate was
found to suppress the pro-inflammatory cytokine interleukin 8 in intestinal
epithelial cells (24;25), and to suppress interleukin 12 and up-regulate interleukin
10 secretion in monocytes (26). Butyrate has also been shown to decrease proinflammatory cytokine mRNA expression and production in inflamed and noninflamed colonic mucosa biopsies as well as in peripheral blood mononuclear
cells (27). Propionate induced leptin production in human adipocytes and in mice
68
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
(22). Butyrate and propionate have been shown to inhibit cytokine-induced
expression of adhesion molecules in endothelial cells (28;29). All three SCFAs
decreased lipopolysacharide-stimulated TNF-α release from human neutrophils
(30).
However, information about effects of SCFAs on cytokine concentrations in
human studies is scarce. We showed that an evening meal of barley kernels
prevented the late postprandial rise in the pro-inflammatory cytokines IL 6 and
TNF-α after an glucose load in the morning (7). Lower IL 6 concentrations have
also be found by Nilsson et al (4) in the morning after a barley kernel evening
meal compared with white wheat bread, however no SCFA concentrations were
measured in this study. In our study higher concentrations of hydrogen in breath
were observed during the whole study period in the morning after the barley
kernel evening meal, while higher plasma butyrate concentrations were observed
only during the first 2 h postprandial. As the suppressive effect on inflammation
factors was observed during 2 – 4 h postprandial a direct relationship between
plasma SCFA concentration and this effect can not be established (7). More
research is needed to confirm the anti-inflammatory properties of SCFAs found in
in vitro studies.
2. SCFAs and lipid metabolism
The relationship between high concentrations of plasma free fatty acids (FFA)
and the development of type 2 diabetes has been well documented (31;32). One
of the strategies to prevent insulin resistance is to reduce plasma FFA (33).
Suppressive effects of SCFAs on FFA, therefore, could be a mechanism by
which SCFAs modulate insulin sensitivity.
Several studies showed that rectal infusion (34;35) as well as intravenous
infusion (36;37) of SCFAs suppress FFA concentrations in humans. SCFAs were
also found to induce lower plasma FFA when SCFAs were administered to
humans by gastric infusion for 3 h at a rate calculated on the basis of a
continuous daily fermentation of 30 g dietary fiber, i.e. acetate (12 mmol/h), or
69
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
propionate (4 mmol/h), or acetate + propionate (12 mmol/h + 4 mmol/h) (38).
Suppression of plasma FFA concentrations was also shown in overweight
volunteers after ingestion of 30 g lactulose, a non-digestible carbohydrate (39).
The involvement of FFA in the effect on glucose tolerance or insulin
sensitivity in second meal studies, however, is not clear as the results concerning
FFA concentrations of these studies are inconsistent (2-4;10).
3. Others
The study of Gao et al (15) gave an indication as to the possible underlying
mechanisms of the insulin sensitizing effect of butyrate. Cell culture experiments
to explore the underlying molecular mechanisms of their observation showed the
potency of butyrate to directly activate among others AMPK. The activation of
AMPK by pharmacological means previously has been shown to increase
glucose transport and cell-surface GLUT 4 content in skeletal muscle from
nondiabetic men (40).
Propionate ingestion was able to reduce fasting blood glucose in rats (14). It
was found in isolated rat hepatocytes that 5 mM of propionate increased glucose
use and reduced glycolysis (41). The effect of SCFAs on blood glucose could
play a role in keeping or improving the insulin sensitivity in a long term since
hyperglycemia was related to insulin resistance.
An association between oxidative stress and insulin resistance has been
established by many studies as indicated in a recent review paper (42). As
SCFAs have been shown to suppress oxidative stress (43) this could be one of
the mechanisms that link SCFAs to insulin sensitivity.
The relevance of the SCFA profile
As described above propionate and butyrate were related in several studies to
improved glucose tolerance and insulin sensitivity. However, no significant
70
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
change was found for insulin sensitivity (euglycemic-hyperinsulinic clamp) by
Alamowitch et al (44) in men who received an ileal infusion of 90 mmol/L of
SCFAs (3.3 ml/min for 18 h). The infusion contained 60% acetate, 25%
propionate and 15% butyrate. Also, an ileal infusion (0.01 mmol/kg BW/min of
SCFAs) containing equal proportion of acetate, propionate and butyrate, for 7 d
in pigs did not improve glucose tolerance as measured by frequently sampled
intravenous glucose tolerance tests (45).
This shows that the effects of infusions of certain mixtures of SCFAs are
different from that of individual SCFA. As fermentation of non-digestible
carbohydrates increases the concentration of all SCFA the observed insulin
sensitizing effect can not be due to a single SCFA but needs to be determined by
a certain SCFA profile. However, due to the inaccessibility of the large intestine,
information of SCFA profiles in vivo after consumption of non-digestible
carbohydrates is not available. The SCFA profiles of different substrates are
mainly obtained from in vitro incubations with human fecal samples. Several
authors reported the rate of fermentation and the concentrations of fermentation
products of various non-digestible carbohydrates (46-50).
From these studies could be derived that substrates with higher solubility,
lower molecular weight, small particle size, and which are easy to be broken
down by digestive enzymes (e.g. pectins, gum arabic, and guar gum) produce
higher proportion of acetate. In contrast, wheat bran, oat fiber, cellulose and
resistant starch are substrates with opposite properties. Those have a lower
fermentation rate and produce higher proportions of butyrate and propionate.
Evening meals that have been reported to induce glucose tolerance the next
morning are mainly meals rich in cereal fiber and resistant starch. Information of
the in vitro incubations thus implies that a SCFA profile with relative high butyrate
and propionate proportions could be beneficial.
Strategies for manipulation of colonic fermentation
71
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
Profiles of SCFAs are mainly determined by the type of substrate, the
composition and activity of the microbiota as well as the small and large intestinal
transit (51-53). Therefore, targets for manipulation of colonic fermentation are the
substrate, the microbiota and the transit.
1. Fermentable substrates
Fermentable substrates can be of exogenous and endogenous origin. The
endogenous fermentable substrates include about 2-3 g/day of small intestinal
mucin and 4-6 g/day of pancreatic proteases. Dietary protein and peptides
contribute 3-9 g/day fermentable substrate. Protein fermentation can account for
about 17% of the SCFAs found in the caecum and for 38% of the SCFAs in the
sigmoid/rectum (54). Dietary carbohydrates contribute the largest amount of
fermentable substrates: 8-18 g/day of non-starch polysaccharides and 8-40
g/day of RS in Western populations.
The chemical and physical structure of the substrate and the combination of
different substrates influence the SCFA profile. In general, substrates with higher
solubility (55), shorter chain length (56), smaller particle size (57), faster transit
(58) are fermented faster. From substrates that are faster fermented higher total
SCFAs and higher proportion of acetate are produced (55) and the fermentation
mainly occurs in the proximal colon. Substrates that are fermented at a slower
rate reach the distal colon and higher proportions of butyrate and/or propionate
are produced.
The combination of different non-digestible carbohydrates could be a feasible
manipulation to obtain an optimal SCFA profile. A less fermentable substrate
may help to shift the fermentable substrate to the distal colon and prolong the
production of SCFAs. From high amylose cornstarch combined with wheat bran
more SCFAs were produced and distal colon fermentation was increased (59) in
rats. When RS was fed in combination with psyllium, the fermentation could be
delayed to the distal part of the colon (60). The combined use of different
substrates may increase the SCFA producing effect of the individual substrates.
72
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
A mixture of long-chain inulin and short-chain-chain fructooligosaccharides
produced more SCFAs than equal amounts of the individual substrates (56).
In healthy volunteers, combined intake of RS and wheat bran resulted in
higher fecal concentration and proportion of butyrate compared to consumption
of only wheat bran (61). A mixture of gum arabic and cellulose generated more
butyrate in the rat hindgut than the individual substrates separately (62). Similar
results were also observed in pigs (63). When pectin and guar were fed to rats
separately, the fecal butyrate concentration was low and fermentation mainly
took place in the caecum. When both substrates were fed together, butyrate
production doubled and the concentration remained high in the distal colon (64).
Given the combined use of different substrates and the possible modulating
approach mentioned above, whole grain food emerges as the model food to
manipulate colonic fermentation in an optimal way. The whole grain food is rich
in fermentable DF and RS which means higher fermentation potential. It also
contains non-fermentable DF. The combined DF and RS may result in higher
butyrate concentration.
2. Composition of microbiota
The role of prebiotics and probiotics in the regulation of colon microbiota has
been extensively reviewed elsewhere (65-68). Different substrates could produce
different metabolites and the same substrate could be fermented by different
microbiota to different metabolites (65). In spite of the fact that microbiota are an
important factor to manipulate SCFAs production this paper is mainly focusing on
the aspect of substrates.
3. Enzyme inhibitors
Enzyme inhibitors such as α-amylase and α-glucosidase reduce the rate and
extent of digestion of dietary carbohydrates and thereby increase the
fermentable amount. Healthy volunteers were given 50 g starch after an
overnight fast. Addition of an amylase inhibitor, compared to that of a placebo,
73
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
increased the amount of total carbohydrates that reached the terminal ileum and
increased breath hydrogen (69).
The percentage of butyrate produced is lower from RS than from digestible
starch (70). Further, acarbose also has been shown to increase colonic transit,
especially that in the ascending and transverse colon (71), which means that the
distribution of SCFAs in colon can be affected by application of acarbose. If an
appropriate amount of acarbose is used, it is possible that colonic fermentation is
increased without impact on other physiological events such as digestion and
absorption of protein and fat.
4. Transit time
Both small intestinal and colonic transit can influence the SCFA profiles. The
small intestinal transit time is inversely related to substrate availability (72-75).
Faster OCTT implies that more carbohydrates escape the small intestinal
digestion and more fermentable substrates reach the colon. In one study (73) it
was seen that loperamide significantly decreased the amount of unabsorbed
starch, while magnesium citrate significantly increased starch malabsorption
when a solid meal containing 50 g potato starch was ingested with either
loperamide or magnesium citrate. It was also shown that metoclopramide (20 mg
p.o) reduced carbohydrate absorption in man in a dose related manner (72). The
effect of the increase in starch malabsorption on SCFA profiles, however, was
not examined.
In other studies, the change of the amount of the substrate was not only
related to the amount of SCFAs produced, but also related to the proportion of
SCFAs. In one study (58), 28 g of wheat bran, senna or loperamide were in turn
given to healthy subjects for 9 days, the whole gut transit time was significantly
shortened by wheat bran and senna, and significantly increased by loperamide.
The total SCFA and butyrate concentrations in stool were significantly increased
by senna and decreased by loperamide. In another study (75) the ileal effluent
was fermented after diets with different RS content. The RS recovered from the
74
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
ileal effluent was inversely related to the small intestine transit time. The high RS
diet caused a higher SCFA production and a higher molar proportion of butyrate
compared with the control diet.
The colon transit time affects the site of fermentation as well as the rate and
extent of absorption of SCFAs (58). As the residing microbiota is different at
different sections of the colon, the fermentation products may also be changed
accordingly (76;77). Colon transit accounts for 90% of the whole gut transit.
Bacterial growth and metabolism are more efficient at faster transit times. An
inverse relationship between mean transit time and the production of total SCFAs
and butyrate was reported (58).
Substrates can change the colonic transit time, and different substrates have
different effects. Lactulose (10-20 mL, 3 times per day) significantly accelerated
transit of ascending colon in healthy subjects, and it also accelerated movement
through the rest of the colon. When the poorly fermentable fiber ispaghula was
added to the lactulose (3.5 g, 3 times per day), the acceleration of the proximal
colon transit was abolished. On the contrary, the readily fermentable gelling
agent guar gum (5 g, 3 times per day) further accelerated the effect of lactulose
(78).
The colonic transit time is influenced by many factors. The ingestion of a meal
stimulates colonic motility. After a meal, the increased ileal flow to the ceacum
increases the availability of substrate which implies immediate increased
fermentation as the caecum is the most active fermentation site. The effect of a
meal on colonic fermentation through changing the colon transit time might
depend on the composition of the meal (79). Two meals, which both contained
4.18 MJ energy from which 60% was either derived from fat or carbohydrates,
were given to healthy subjects; the carbohydrate-rich meals induced a colonic
motor response, but the effects were short lasting when compared with that of
the fat-rich meals. Fat induced motor activity persisted longer than that of the
carbohydrate meal, but the onset of motor response was slower.
75
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
Colonic transit was shown to be slower during nocturnal sleep than during
daytime and awakening increased the transit. Small particles transits faster than
bigger particles (80).
There are many additional factors related to gastro-intestinal transit time,
which are not possible to manipulate but they should be taken into account.
Gastric emptying, small intestinal transit and colonic transit were delayed in
women (81;82). Colonic transit was also delayed in old people and people with
constipation. Small bowel transit is not different between health young women
and old women, but colonic fermentation was more pronounced in the young as
reflected by breath hydrogen (83). Women had significantly longer transits than
men for the whole, right and rectosigmoid colon and near to statistical
significance for left colon (84).
Discussion and conclusion
Insulin resistance is a key factor for the development of type 2 diabetes,
cardiovascular diseases and other chronic diseases. People are encouraged to
increase their intake of non-digestible carbohydrates and to consume low GI
food (85). Increased dietary carbohydrates intake will possibly also increase the
amount of substrates for colonic fermentation and consequently SCFA
production. Dietary recommendation based on not only GI, but also colonic
fermentation properties might produce maximal beneficial effects.
The metabolic effect of colonic fermentation is determined by the SCFA
profile. The maximal beneficial effect and the optimal SCFA profile can not be
clearly defined. To what extent the peripheral plasma SCFA profile reflects the
colonic profile is not yet known. Also, the SCFA profile from different animal
models are different (86) and may be different from that in humans. The
information about SCFA profiles of different substrates obtained by in vitro
fermentation may also not reflect the one in vivo. Therefore, although the
76
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
available data showed that manipulation of colonic fermentation is feasible, the
effective SCFA profile should be further identified.
Concerning the prevention of insulin resistance by certain SCFA, some
evidence is available. The higher butyrate or propionate concentration in plasma
in the morning after evening meals rich in non-digestible carbohydrates(7) as
well as the beneficial effects observed after direct administration of those SCFA
(13-17) point in the same direction.
In summary, this review indicates that colonic fermentation of non-digestible
carbohydrates could be an important factor related to the beneficial effect of
dietary carbohydrates. Manipulation of colonic fermentation through nondigestible carbohydrates, especial whole grain food or food with properties of
whole grain could be a feasible approach to improve insulin resistance and
insulin resistance related diseases. The relation between SCFA profile and
insulin sensitivity need to be further investigated.
77
APPENDIX 2. MANIPULATIN OF COLONIC FERMENTATION
Table 1 Short-chain fatty acids profile of fermentable substrates
Total
unit of total SCFAs
SCFAs
Ratio of SCFAs
Species
Ace
Pro
But
In vitor or
in vitro
Reference
Sugar beet fiber
1160
umol/g substrate
93
7
1
human
in vitro
(47)
Citrus pectin
6403
umol/g substrate
90
7
3
human
in vitro
(47)
Oat fiber
369
umol/g substrate
88
12
1
human
in vitro
(47)
umol/110g substrate
84
7
10
human
in vitro
(49)
Pectin (sub 1)
Glucose
220
mmol/L
83
7
10
human
in vitro
(87)
Pectin
48
mmol/L
82
9
10
human
in vitro
(87)
Pectin
26
mmol/L
81
10
9
human
in vitro
(87)
umol/111g substrate
81
7
12
human
in vitro
(49)
Pectin (sub 2)
Apple pectin
6982
umol/g substrate
80
11
8
human
in vitro
(47)
Pectin
80
mmol/L
80
9
12
human
in vitro
(87)
Control
60.07
mmol/g
78
12
10
rat
in vivo
(88)
CS (sub 4)
597
umol/106g substrate
77
9
13
human
in vitro
(49)
CB (sub 4)
657
umol/107g substrate
77
15
9
human
in vitro
(49)
no treatment
11.43
mmol/L
77
13
11
human
in vitro
(60)
CB (sub 2)
562
umol/103g substrate
75
14
11
human
in vitro
(49)
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
Total
unit of total SCFAs
Ratio of SCFAs
SCFAs
Species
Ace
Pro
But
In vitor or
in vitro
Reference
Pectin
153
mmol/L
75
9
16
human
in vitro
(87)
CB (sub 3)
605
umol/105g substrate
75
16
9
human
in vitro
(49)
CB (sub 1)
559
umol/101g substrate
74
16
10
human
in vitro
(49)
Pea fiber
761
umol/g substrate
74
17
9
human
in vitro
(47)
Pectin
172
mmol/L
74
7
20
human
in vitro
(87)
Control
38.7
mmol/L
72
16
12
human
in vitro
(75)
CS (sub 2)
635
umol/102g substrate
72
6
22
human
in vitro
(49)
Soy fiber
1943
umol/g substrate
71
21
8
human
in vitro
(47)
Pectin
114
mmol/L
71
12
17
human
in vitro
(55)
no-fiber
98.8
mmol/L
71
24
5
rat
in vivo
(89)
umol/117g substrate
68
7
19
human
in vitro
(49)
umol/104g substrate
70
8
22
human
in vitro
(49)
umol/112g substrate
70
9
19
human
in vitro
(49)
Oat bran (sub 2)
CS (sub 3)
680
Potato starch (sub 1)
Glucose
163
mmol/L
67
12
21
human
in vitro
(87)
CS (sub 1)
747
umol/100g substrate
65
12
23
human
in vitro
(49)
Lactose
10
mmol/g CHO
69
12
18
human
in vitro
(90)
79
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
Total
unit of total SCFAs
Ratio of SCFAs
SCFAs
Xylan (sub 1)
Species
Ace
Pro
But
umol/114g substrate
67
14
19
In vitor or
in vitro
Reference
human
in vitro
(49)
Wheat bran
31
mmol/L
63
14
21
human
in vitro
(87)
Starch (resistant)
85
mmol/L
69
14
17
human
in vitro
(55)
umol/118g substrate
67
14
17
human
in vitro
(49)
Wheat bran (sub 1)
Faecal conc.
128
mmol/L
70
15
11
human
in vitro
(87)
Wheat bran
21
mmol/L
66
15
16
human
in vitro
(87)
Parboiled rice bran
101
mmol/L
66
16
18
rat
in vivo
(89)
Williamson oat fiber
0.42
mmol/L
62
17
21
human
in vitro
(91)
Extruded rice bran
105
mmol/L
67
17
16
rat
in vivo
(89)
umol/116g substrate
60
17
20
human
in vitro
(49)
mmol/L
66
18
17
human
in vitro
(75)
umol/119g substrate
60
18
20
human
in vitro
(49)
Oat bran (sub 1)
High RS diet
67
Wheat bran (sub 2)
Glucose
90
mmol/L
63
18
17
human
in vitro
(87)
Glucose
120
mmol/L
61
18
21
human
in vitro
(55)
0.87
mmol/L
60
18
22
human
in vitro
(91)
Canadian
Harvest
oat
fiber
80
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
Total
unit of total SCFAs
Ratio of SCFAs
SCFAs
Species
Ace
Pro
But
In vitor or
in vitro
Reference
Cellulose
5
mmol/L
65
19
13
human
in vitro
(87)
Cellulose
23
mmol/L
61
19
16
human
in vitro
(87)
Starch
2
mmol/g CHO
61
19
19
human
in vitro
(90)
Soy fiber
5.75
mmol/L
65
19
16
human
in vitro
(91)
0.41
mmol/L
68
20
13
human
in vitro
(91)
Glucose
35
mmol/L
64
20
17
human
in vitro
(87)
Fiber form (wheat bran)
54
mmol/L
58
20
22
human
in vitro
(55)
Control
54
mmol/L
57
22
13
human
in vitro
(87)
Glucose
26
mmol/L
62
22
17
human
in vitro
(87)
Albumin
161
mmol/L
41
22
20
human
in vitro
(87)
Gum arabic
8.19
mmol/L
66
23
11
human
in vitro
(91)
Psyllium
3
mmol/L
70
24
6
human
in vitro
(91)
Cellulose
6
mmol/L
63
24
11
human
in vitro
(87)
Cellulose
5
mmol/L
59
24
14
human
in vitro
(87)
Albumin
119
mmol/L
41
24
19
human
in vitro
(87)
Carboxymethyl
cellulose
(CMCA)
81
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
Total
unit of total SCFAs
Ratio of SCFAs
SCFAs
Species
Ace
Pro
But
In vitor or
in vitro
Reference
Inolaxol (sterculia)
41
mmol/L
59
24
16
human
in vitro
(55)
AP40
15.1
mmol/g
64
25
11
rat
in vivo
(88)
Methylcellulose
19.6
mmol/L
67
25
8
rat
in vivo
(89)
Cellulose
36
mmol/L
59
25
16
human
in vitro
(55)
Control
32
mmol/L
57
25
18
human
in vitro
(55)
Cellulose
11
mmol/L
47
25
22
human
in vitro
(87)
Gum arabic
6936
umol/g substrate
66
25
8
human
in vitro
(47)
Inulin
38.9
mmol/L
60
26
14
human
in vitro
(60)
Ileostomy effluent
6.7
mmol/g CHO
63
27
10
human
in vitro
(90)
Albumin
55
mmol/L
35
27
18
human
in vitro
(87)
GA40
25.28
mmol/g
64
27
9
rat
in vivo
(88)
Albumin
23
mmol/L
44
28
14
human
in vitro
(87)
Albumin
34
mmol/L
39
28
16
human
in vitro
(87)
Barley 130 kDa
36.71
mmol/L
53
28
19
human
in vitro
(60)
AP20
23.26
mmol/g
65
28
7
rat
in vivo
(88)
Gum gum 20 kDa
60.9
umol/mL
66
28
5
human
in vitro
(46)
82
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
Total
unit of total SCFAs
Ratio of SCFAs
SCFAs
Species
Ace
Pro
But
In vitor or
in vitro
Reference
Arabic-guar mixture
7404
umol/g substrate
61
29
10
human
in vitro
(47)
Ispaghula
28
mmol/L
66
29
7
human
in vitro
(87)
umol/108g substrate
57
29
12
human
in vitro
(49)
Arabinogalactan (sub 1)
GA20
21.87
mmol/g
64
29
7
rat
in vivo
(88)
Corn bran
646
umol/g substrate
62
29
9
human
in vitro
(47)
Vi-Siblin(ispaghula)
76
mmol/L
60
29
11
human
in vitro
(55)
Lunelax(ispaghula)
79
mmol/L
60
29
11
human
in vitro
(55)
Oats 230 kDa
32.78
mmol/L
51
30
18
human
in vitro
(60)
without A
51.1
umol/g wet wt
44
31
24
human
in vivo
(92)
Ispaghula
14
mmol/L
61
32
8
human
in vitro
(87)
Barley 243 kDa
38.82
mmol/L
52
33
15
human
in vitro
(60)
Guar
7.5
mmol/g CHO
55
33
11
human
in vitro
(90)
Barley 172 kDa
47.46
mmol/L
46
34
20
human
in vitro
(60)
Oat fiber
159
umol/g substrate
43
35
23
human
in vitro
(47)
Oats 150 kDa
43.57
mmol/L
52
35
13
human
in vitro
(60)
Ispaghula
45
mmol/L
58
35
8
human
in vitro
(87)
83
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
Total
unit of total SCFAs
Ratio of SCFAs
SCFAs
Ispaghula
92
Arabinogalactan (sub 2)
Species
In vitor or
in vitro
Reference
human
in vitro
(87)
11
human
in vitro
(49)
Ace
Pro
But
mmol/L
54
35
11
umol/109g substrate
54
35
Ispaghula
63
mmol/L
55
37
9
human
in vitro
(87)
Guar gum 1100 kDa
62.8
umol/mL
54
40
6
human
in vitro
(46)
Guar gum 400 kDa
68.6
umol/mL
49
41
10
human
in vitro
(46)
Guar gum 15 kDa
47.9
umol/mL
46
44
10
human
in vitro
(46)
GA80
5.48
mmol/g
36
44
19
rat
in vivo
(88)
AB80
6.82
mmol/g
43
53
5
rat
in vivo
(88)
Rhamnose
9
mmol/g CHO
44
53
2
human
in vitro
(90)
AP80
6.11
mmol/g
42
54
4
rat
in vivo
(88)
Coars wheat bran
61.7
mmol/L
53
22
25
rat
in vivo
(89)
without A
6.8
umol/g wet wt
46
29
25
human
in vivo
(92)
Wheat bran
125
mmol/L
58
11
28
human
in vitro
(87)
umol/115g substrate
61
9
29
human
in vitro
(49)
Xylan (sub 2)
Wheat bran
48
mmol/L
56
13
29
human
in vitro
(87)
Wheat bran
82
mmol/L
56
11
30
human
in vitro
(87)
84
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
Total
unit of total SCFAs
Ratio of SCFAs
SCFAs
Potato starch (sub 2)
Species
Ace
Pro
But
umol/113g substrate
64
4
31
In vitor or
in vitro
Reference
human
in vitro
(49)
with A
61.2
umol/g wet wt
45
23
32
human
in vivo
(92)
Fine wheat bran
56.2
mmol/L
41
25
34
rat
in vivo
(89)
with A
13.8
umol/g wet wt
44
21
35
human
in vivo
(92)
A, acarbose; Ace, acetate; AP, gum A. pycnantha; AB, gum A. baileyana; But, butyrate; CB, cabbage fiber;
CS, corn starch; GA, gum arabic; Pro, propionate; sub, subject; RS, resistant starch; SCFAs, short-chain
fatty acids.
85
APPENDIX 2. MANIPULATION OF COLONIC FERMENTATION
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95
APPENDIX 3. ESTIMATION OF FERMENTATION
APPENDIX 3
A curve fitting approach to estimate the extent of
fermentation of indigestible carbohydrates
Hongwei Wang
Desirée Weening
Elles Jonkers
Theo Boer
Frans Stellaard
Alexandra Small
Tom Preston
Roel J. Vonk
Marion G. Priebe
Adapted from: European Journal of Clinical Investigation 2008;38:863868.
96
APPENDIX 3. ESTIMATION OF FERMENTATION
Abstract
Background
Information about the extent of carbohydrate digestion and fermentation is critical
to our ability to explore the metabolic effects of carbohydrate fermentation in
vivo. We used cooked
13
C-labelled barley kernels, which are rich in indigestible
carbohydrates, to develop a method which makes it possible to distinguish
between and to assess carbohydrate digestion and fermentation.
Materials and Methods
Seventeen volunteers ingested 86 g (dry weight) of cooked naturally
enriched barley kernels after an overnight fast.
13
C
13
CO2 and H2 in breath samples
were measured every half hour for 12 h. The data of
13
CO2 in breath before the
start of the fermentation were used to fit the curve representing the digestion
phase. The difference between the area under curve (AUC) of the fitted digestion
curve and the AUC of the observed curve was regarded to represent the
fermentation part. Different approaches were applied to determine the proportion
of the 13C-dose available for digestion and fermentation.
Results
Four h after intake of barley, H2-excretion in breath started to rise. Within 12 h,
24-48% of the
13
C-dose was recovered as
13
CO2, of which 18-19% was derived
from colonic fermentation and the rest from digestion. By extrapolating the curve
to baseline, it was estimated that eventually 24-25% of the total available
13
C in
barley would be derived from colon fermentation.
Conclusion
Curve fitting, using
13
CO2- and H2- breath data, is a feasible and non-invasive
method to assess carbohydrate digestion and fermentation after consumption of
13
C enriched starchy food.
97
APPENDIX 3. ESTIMATION OF FERMENTATION
Introduction
Food components that escape small intestinal digestion can be fermented in the
colon. Dietary carbohydrates, specifically resistant starch (RS) and soluble
dietary fiber (SDF), are potential substrates for fermentation. Accumulating
evidence indicates that fermentation is related to many physiological effects
through the production of short-chain fatty acids as metabolically active signalling
agents (1;2). In some studies, colonic microbial activity has also been linked to
the development of obesity (3;4). However, colon fermentation is a complex
process which is determined by a cluster of factors including substrate
properties, microbiota profile and activity as well as intestinal transit time and
absorption capacity of the colonic epithelial cells. Both the digestive phase and
the fermentative phase are related to nutrient bio-availability. In order to analyse
the contribution of the digestive as well as of the fermentative phase to the
overall biological effects of starchy food, we need to know the extent of digestion
and fermentation of the specific carbohydrates.
Various methods have been developed to measure carbohydrate digestion
and fermentation including the H2-breath test, ileostomy and intubation of the
ileum. However, the H2-breath test is dependent on the composition of the
colonic microbiota and the type of fiber (5), and is only semiquantitative (6).
Radioscopy may be harmful, and in the case of ileostomy and intubation
disturbed intestinal functions are likely (7).
The
13
CO2-breath test can be used to quantitatively monitor digestion of
13
C-
labelled starch because a considerable portion of digested carbohydrate is
oxidised (8). However, after the arrival of indigestible carbohydrates in the colon,
both small intestinal digestion and colonic fermentation are contributing to breath
13
CO2. Previous studies have attempted to measure the extent of starch
digestion and fermentation by mathematic modelling in infants (9) and adults (10)
using
13
CO2 data only. As the increase of breath H2 indicates the arrival and
fermentation of indigestible carbohydrates in the colon (11), a combination of the
98
APPENDIX 3. ESTIMATION OF FERMENTATION
two tests seems useful for a non-invasive approach (12) and improved
modelling. Our aim was to develop a curve fitting approach for the estimation of
the extent of carbohydrate digestion and fermentation by applying a combined
13
CO2- and H2-breath test.
Subjects and methods
Subjects
Seventeen healthy volunteers (7 male, 10 female) aged 20.9±0.3 (Mean±SEM)
years, with a Body Mass Index of 21.1±0.4 (kg/m2) took part in the study. The
criteria for exclusion were current use of any medications, use of antibiotics in
the
last
3
months,
gastrointestinal
symptoms,
diabetes
mellitus
and
gastrointestinal surgery. Written informed consent was obtained from all
subjects. Approval was obtained from the Medical Ethics Committee of the
University Medical Centre in Groningen.
Test meal
Spring barley (var Kirsty) was grown in 20 cm pots in a greenhouse. Upon
anthesis, pots were moved to 600 L growth cabinets (Fitotron; Fisons UK) which
had been sealed from the atmosphere.
Approximately 1 L
13
CO2 (Spectra
Gases, NJ, USA) was added to each growth cabinet. After 24 h incubation, pots
were returned to the greenhouse until harvest. The
13
C abundance of the barley
was determined by isotope-ratio mass spectrometry to be 1.128 atom %
13
C.
Percentage carbon was also estimated by this technique to be 39.6%. Eighty six
gram of barley (estimated amount available carbohydrate (AC), RS and SDF: 50
g, 7.5 g, 4.4 g respectively (13) )were cooked in 250 ml water with 1 g salt till all
water was absorbed (35 min).
Study protocol
99
APPENDIX 3. ESTIMATION OF FERMENTATION
The subjects were asked to refrain from alcohol and strenuous exercise for 24 h
before each study day. At 8:00, after an overnight fast, volunteers consumed the
test meal within 30 min with 250 mL of water. Breath samples were collected by
breathing through a straw into 10-mL tubes. Three breath samples were
obtained before consumption of the test meal and thereafter every half hour for
12 h. A standard lunch, consisting of two white bread rolls with ham or cheese,
was provided to the subjects 4 h after intake of the test meal. All the subjects
were dismissed after 2 h when normal daily activities were resumed, except that
volunteers were asked neither to engage in intensive physical exercise, nor to
consume 13C enriched food and to take their dinner 9 h after the intake of the test
meal.
Analytical procedures
Analysis of
13
C abundance in breath CO2 was performed using gas
chromatography isotope ratio mass spectrometry (GC/IRMS) (TracerMAT,
Thermo Finnigan, Bremen, Germany) measuring the
13
C/12C ratio versus the
international standard Pee Dee Belemnite (δ13CPDB) in ‰. Breath H2 was
measured using gas chromatography with a thermal conductivity detector (HP
6890 Agilent, Hewlett Packard Co, Palo Alto, USA), using a CP-Molsieve 5A
column of 25 m×0.53 mm (50 μm film thickness) (Chrompack International B.V.,
Bergen op Zoom, The Netherlands).
Calculations and curve fitting
Physical activity level (PAL) is a parameter to express the energy expenditure
level based on a person’s physical activity. During the first 6 h the volunteers
were sitting and we estimated a PAL of 1.0. The last 6 h the volunteers could go
home and we assumed light activity in this phase which means that the PAL will
be about 1.2 times that of the PAL during the first 6 h (14;15). As it was also
suggested that energy expenditure for adults under experimental situations
similar to that of the first 6 h in this study were above resting and PAL would be
100
APPENDIX 3. ESTIMATION OF FERMENTATION
1.3 instead of 1.0 (16), we also used a PAL of 1.3 and 1.56 for the rest and
active period respectively for our calculations. CO2 production was assumed to
be 300 mmol/m2 body surface area (BSA) per hour at rest (17). BSA was
calculated according to the classic weight-height formula of Haycock et al (18).
Resting CO2 production was multiplied by PAL to give total CO2 production.
The
13
CO2 excretion in breath was expressed as percentage of the
administered dose per hour (%dose/h) and as a cumulative percentage of the
administered dose (cum %dose) over time. The following approaches were used
to define the administered 13C-dose:
1. Percentage carbon-atoms present in the sample (C%)
The percentage of carbon-atoms in the barley samples was measured to
be 39.6% which was used for the calculation of the dose (2.84 moles
carbon).
2. Dose of available carbohydrate (ACHO)
As the barley meals contained 50 g available carbohydrate this amount
was used to calculate the 13C-dose (1.85 moles carbon).
3. Dose of total carbohydrates available for digestion and fermentation
(TCHO)
13
C dose was calculated based on the total amount of available
carbohydrates in the test meal, thus digestible carbohydrate, RS and
SDF. This was estimated to be 61.9 g (2.29 moles carbon).
The H2 results were expressed as parts per million (ppm). A sustained
increase in H2 of more than 10 ppm was regarded to indicate the arrival of
carbohydrates in the colon (19). We regarded the time point before this increase
as starting point of fermentation. Time from intake of test meal to this point was
regarded as the oro-cecal transit time (OCTT).
The data of the %dose/h were used to fit a curve with the equation (20): y = a
× (t + d)b × e -c × (t + d). In the equation, y is the %dose/h; t is time; a, b, c and d are
101
APPENDIX 3. ESTIMATION OF FERMENTATION
constants. The curve was fitted with Solver of Microsoft Excel 2003. In principle,
initial values of constants a, b, c and d were specified based on previous
experience and then optimized by minimizing the value of the squared sum of
the difference between data and fit. Breath
13
CO2 data before the time point of
start of fermentation were used to fit the curve representing the digestion phase.
The areas under the curves (AUC) were calculated for the observed and fitted
curves. The difference between both AUCs resulted in the fermentation curve.
The 12 h 13CO2 data were used to fit a curve to baseline describing eventual total
13
CO2 appearance. Curves were fitted separately for all the 17 subjects and the
average values calculated.
It was assumed that the recovered part of
13
C represents the fate of all the
available 13C in the test meal and that protein and fat present in the test meal are
digested completely in the small intestine. Food tables state that pearled raw
barley grain contains 77% carbohydrate, 10% protein and 1% fat (21). Using
accepted stoichiometry, this gives a theoretical content of 41% carbon or 3 moles
carbon per 86 g. Also, we assumed that the colon is the main source of breath
H2 and colonic H2 will be excreted in breath immediately after production (11).
Results
Hydrogen
H2 in breath increased significantly at 252 ± 15 min after intake of the test meal
and remained at least 10 ppm above baseline level during the whole study
period (Figure 1).
102
APPENDIX 3. ESTIMATION OF FERMENTATION
Figure 1 Mean ( SEM) of breath H2 in 17 healthy subjects after ingestion of 86
g (dry weight) intrinsically 13C-labelled barley kernels.
H2 excretion (ppm)
60
50
40
30
20
Dinner
10
OCTT
Lunch
0
0
60
120
180
240
300
360
420
480
540
600
660
720
780
Time (min)
13
CO2
After intake of the test meal, breath
13
CO2 increased steadily and 12 h after
intake of the test meal, 29.9% of the administrated 13C was recovered (Figure 2).
Data for Figure 2, 3 and 4 were calculated based on values of PAL 1.0 (first 6 h)
and 1.2 (last 6 h) and TCHO as 13C-dose.
Curve fitting and calculations
Figure 3 shows the course of the fitted curve for the digestion phase and for the
fermentation phase. This fermentation curve was obtained by subtracting fitted
digestion data from the observed data, whereas the fermentation curve shown in
Figure 4 was obtained by subtracting the fitted digestion data from the fitted total
data. Figure 4 depicts the mean values and SEM of cum%dose, fitted total
103
APPENDIX 3. ESTIMATION OF FERMENTATION
curve, fitted digestion curve and fermentation curve. All these curves were
extrapolated to a time of 24 h after intake of the test meal. About 24 h after the
intake of the test meal, breath
13
CO2 has returned to baseline. Furthermore, the
predicted cum%dose reaches a plateau value 18 h after intake of the test meal
(Figure 4).
The results based on different definitions of the administered
13
C-dose and
PALs were shown in Table 1. The cum%dose over 12 h lay between 23 and
48%. From that 18-19 % accounted for fermentation and it was estimated, based
on the exploration of the curves to baseline, that eventually 24-25 % of the total
cum%dose after 24 h would be derived from fermentation.
Figure 2
13
CO2 excretion in breath as mean ( SEM) percentage of administered
dose per hour and mean ( SEM) cumulative percentage of the administered
dose, in 17 healthy subjects after ingestion of 86 g (dry weight) intrinsically
13
C-
labelled barley kernels.
% dose/h
6
25
5
20
4
15
3
10
2
5
1
0
0
CO 2 (%dose/h)
% dose cum.
13
cum%dose
30
0
60
120
180 240 300 360 420 480 540 600
Time (min)
104
660 720 780
APPENDIX 3. ESTIMATION OF FERMENTATION
Figure 3 Digestive and fermentation curves fitted with data of mean percentage
of the administered dose per hour in breath samples of 17 healthy subjects after
ingestion of 86 g (dry weight) intrinsically 13C-labelled barley kernels.
5
60
Observed curve
Hydrogen
40
3
30
2
20
1
10
0
0
0
60
120
180
240
300
360
420
Time (min)
105
480
540
600
660
720
780
H2 excretion (ppm)
50
Fermentation curve
4
13
CO 2 (%dose/h)
Fitted digestion curve
APPENDIX 3. ESTIMATION OF FERMENTATION
Figure 4 Extrapolated total curve, digestion curve, fermentation curve and
cum%dose curve for the mean ( SEM) of 17 healthy subjects after ingestion of
86 g (dry weight) intrinsically 13C-labelled barley kernels. Data after 720 min were
extrapolated.
Extrapolated part
35
3
Observed curve
Fitted digestion curve
Fitted curve (total)
2
cum%dose
25
20
15
10
1
5
0
0
Time (min)
106
cum%dose
30
60
12
0
18
0
24
0
30
0
36
0
42
0
48
0
54
0
60
0
66
0
72
0
78
0
84
0
90
0
96
0
10
20
10
80
11
40
12
00
12
60
13
20
13
80
14
40
15
00
13
40
4
0
CO 2 (%dose/h)
5
APPENDIX 3. ESTIMATION OF FERMENTATION
Table1. Results calculation the cumulative percentage of the administered dose
using different approaches 1
13
C-dose calculated from
PAL2 (1st 6nd
h–2
6-h)
ACHO3
TCHO4
C%5
37.0 ± 1.6
29.9 ± 1.3
23.4 ± 1.0
19.0 ± 2.2
18.2 ± 2.4
18.0 ± 2.5
24.8 ± 3.0
23.8 ± 3.2
25.1 ± 3.2
48.1 ± 2.1
38.9 ± 1.7
30.4 ± 1.3
18.7 ± 2.0
18.9±2.2
18.6±2.1
25.4 ± 2.6
25.0 ± 3.0
24.6 ± 2.7
total (cum%dose
1.0 – 1.2
after 12h)
% fermentation from
total (12 h)
% fermentation
from predicted total
(extrapolated to 24 h)
total (cum%dose
1.3 – 1.56
after 12h)
% fermentation from
total (12 h)
% fermentation
from predicted total
(extrapolated 24 h)
1
Results are expressed as mean ± SEM.
PAL: physical activity level.
3
ACHO: dose of available carbohydrate.
4
TCHO: dose of total carbohydrates available for digestion and fermentation.
5
C%: percentage carbon-atoms present in the sample.
2
107
APPENDIX 3. ESTIMATION OF FERMENTATION
Discussion
Stable isotope breath tests have proven to be a safe, simple and non-invasive
13
C-
method to investigate gastrointestinal function and metabolism. Oxidation of
labelled carbohydrate following digestion and fermentation can be evaluated by
measuring
13
CO2 excretion in the breath (22). With mathematical models, it is
possible to distinguish between
13
CO2 produced by small intestine digestion and
that by colonic fermentation (9;10;20). By combining breath H2 and
13
CO2
measurements with a curve fitting approach we were able to quantitatively
assess the digestion and fermentation of carbohydrates in a starchy food.
Depending on the different approaches used to estimate the
13
C-dose and the
PAL the cum%dose recovered varied between 23 and 48 %. From this dose
recovered it appeared that 18-19 % was due to the colonic fermentation of
carbohydrates, which was estimated to increase to 24-25 % if measurement
would have been extended to 24 h. (Table 1). Only one study could be found
using a similar approach in two adults. This study reported a cum%dose
recovered of 28% in one subject and 40 % in the other subject after consumption
of 13C-enriched pea flour over a time period of 18 h (10). Although these data are
similar to ours, the difference in test meals and study design and duration makes
direct comparison difficult.
In this study we explored the impact of different estimations of CO2 output on
the study results. In addition, different approaches to calculate the
13
C dose that
can theoretically be recovered in breath CO2 were used. Our data show that
those variations only influence the total cum%dose. The proportion derived from
fermentation remained in a narrow range for both the 12 h measurement and the
extrapolated 24 h estimate. Thus, it can be concluded that the calculation of the
relative contribution of fermentation is robust across different approaches for the
calculation of the total cum%dose and CO2 output. Direct measurement of CO2
output in future studies would help to constrain this variable (16).
108
APPENDIX 3. ESTIMATION OF FERMENTATION
Concerning the calculation of the
13
C dose we acknowledge that there was
also about 10% labelled protein present in the test meal, which could have
influenced our results. Given that protein will largely be digested and possibly
only a third of the assimilated amino acids would be oxidised during the study
(23;24), we would expect a minor contribution from protein to the
13
CO2
production during digestion and very little more during the fermentation phase.
The combination of the
13
CO2- and H2 breath test has been proven to be of great
value. After consumption of the labelled barley kernels an immediate rise in
13
CO2 was seen, which did not return to baseline during the study period.. From
this curve the start of the fermentation could not be derived because the
contribution of fermentation was apparently not late enough or high enough to
result in a discrete second peak. This observation is different to that of a study
which observed a distinct second rise in
13
CO2 excretion starting 432 ± 15 min
after ingestion of 40 g resistant cornstarch (12). Therefore, in studies using
common food products, which normally have a lower content of indigestible
carbohydrates, measurement of breath H2 may be necessary to apply curve
fitting. Breath H2 excretion started at 252 ± 15 min and showed surprisingly little
variation between subjects. This might be due to the fact that subjects received a
subsequent meal 240 min after intake of the test meal. Because the intake of a
meal stimulates the movement of the intestine, indigestible material is moved
distally and increases fermentation (11). Our previous studies showed that a
subsequent meal may influence the OCTT of a liquid test meal (25), but not a
solid test meal (26). The discrepancy between our studies may lay in the
difference of presence of indigestible solids as caloric content of the test meals
was similar (330 kcal previous, 300 kcal present). McIntrey et al (27) observed a
pronounced OCTT acceleration of 95 min after addition of bran (2-5 mm particle
size) to the rice porridge test meal. This suggests that indigestible solids present
in barley could be the cause of the rapid OCTT, with the subsequent meal
causing their movement distally.
109
APPENDIX 3. ESTIMATION OF FERMENTATION
In summary, curve fitting using data from measurements of
13
CO2 and H2 in
breath samples could be a feasible, objective and non-invasive method to
quantitatively assess digestion and fermentation of different starchy food. In
addition, with this approach, it would be possible to examine individual
differences in recovery of energy from colon after starchy food consumption,
through simultaneous estimation of short chain fatty acid production (28). As it
has been shown that the number of bacteria with the capability to break down
indigestible dietary carbohydrates is positively related to obesity (3;4), this could
facilitate investigating the relationship between the degree of colonic
fermentation and the development of obesity.
Acknowledgements
This work was financially supported by the Commission of the European
Communities, and specifically the RTD programme ‘Quality of Life and
Management of Living Resources’, QLK 1-2001-00431 ‘Stable isotope
applications to monitor starch digestion and fermentation for the development of
functional foods’ (EUROSTARCH). This work does not necessarily reflect its
views and in no way anticipates the Commission’s future policy in this area.
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APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
APPENDIX 4
Type of non-digestible carbohydrate affects
plasma and urine short chain fatty acid profiles
in healthy volunteers
Kristin Verbeke
Veronique Ferchaud-Roucher
Tom Preston
Alexandra C. Small
Liesbet Henckaerts
Michel Krempf
Hongwei Wang
Roel J. Vonk
Marion G. Priebe
Adapted form: Eur J Clin Nutr. 2010;64:678-84.
114
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
Abstract
Health effects of whole grain foods are becoming more evident. In this study, we
analyzed the colonic fermentation process of
barley grown under
13
C barley meals, prepared from
13
CO2-atmosphere. In a cross over study 5 volunteers
ingested intact barley kernels (high content of dietary fiber and resistant starch)
and barley porridge (high content of dietary fiber only). Using a newly developed
stable isotope technology we monitored 14 h postprandially
propionate and
13
C-acetate,
13
C-
13
C-butyrate in plasma and urine. The oral-cecal transit time of
the meals was measured with the hydrogen breath test and the
14
CO2-inulin test.
The oro-cecal transit time was 6 h and did not differ between the two test meals.
An increase of
13
C-acetate was observed already early after ingestion of the
meals (< 6 h) and was attributed to early fermentation of the test meal. A rise in
13
C-propionate in the fermentation phase could only be detected after the
porridge and not after the kernels meal. An increase in
13
C-butyrate was only
found in the fermentation phase and was higher after the barley kernels. Urine
13
C-short chain fatty acids data were consistent with these observations. In
conclusion: The difference in the profiles of
13
C-acetate,
13
C-propionate and
13
C-
butyrate indicates that dietary fiber combined with resistant starch results in an
altered fermentation profile than dietary fiber alone.
115
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
Introduction
Whole grain foods have been associated in epidemiological studies with the
prevention of cardiovascular disease, obesity and type 2 diabetes (1-3). One of
the characteristics of whole grain foods is their relatively high content of resistant
starch (RS), if unprocessed (4), and of non-starch polysaccharides (NSP). These
components were related in intervention studies to improved insulin sensitivity (58) and glucose tolerance (9-11). Furthermore, effects on lipid metabolism in
adipocytes (12;13) and postprandial lipid oxidation (14) are reported. However,
the exact mechanism underlying these processes is not clear. It is likely that
those effects of NSP and RS are mediated through fermentation of RS and NSP
in the colon which results in the production of the short-chain fatty acids (SCFA)
acetate, propionate and butyrate. There is emerging evidence that SCFA not
only exert effects in the colonic epithelial cells but also enter the circulation and
can influence metabolic processes in other tissues and organs. Effects of acetate
and propionate on glucose and lipid metabolism in the liver have been reported
(15-20). Acetate is used in the liver as substrate for the synthesis of cholesterol
and fatty acids whereas it seems that propionate inhibits these processes (21).
Effects of SCFA on adipose tissue metabolism are likely as SCFA have been
identified to be ligands for the orphan G protein-coupled receptor GPR41 which
is primarily expressed in adipose tissue (22) and stimulation of leptin production
in adipocytes by propionate (not acetate) has been described (23). Since
adipose tissue is known to secrete various signalling peptides influencing among
others insulin sensitivity, feeding behaviour and inflammation (24) it could
constitute an important link between SCFA production and peripheral metabolic
effects. Furthermore, a possible importance of products of colonic fermentation in
the aetiology of obesity has recently emerged in a study that showed that
microbiota of obese mice was more capable of fermenting non-digestible dietary
polysaccharides which consequently extracted more calories from the food than
the microbiota of lean mice (25).
116
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
To be able to investigate the interaction between colonic SCFA production
and peripheral tissue it is necessary to obtain information about the extent to
which fermentation derived SCFA reach systemic circulation. Furthermore, as
different SCFA can exert different and even opposing effects, information is
needed as to how different fermentable substrates affect these SCFA patterns in
the circulation.
Recently, we developed new analytical methods to determine low-level
enrichment of
13
C-SCFA in human plasma and urine (26). These methods were
applied to test the hypothesis that ingestion of a test meal with a high content of
NSP results in a different
addition contains RS.
13
C-SCFA plasma profile than a test meal that in
13
C-enriched barley was obtained by culturing plants under
13
CO2 enriched atmosphere for the test meals. The test meals administered were
intact cooked barley kernels (high RS and NSP) and barley porridge (high NSP).
Materials and methods
Subjects
Five healthy volunteers with a mean age of 25 ± 7 y participated in the study.
None of the subjects had a history of gastrointestinal or metabolic disease or
previous surgery (except for appendectomy). The subjects were free of
antibiotics or any other medical treatment influencing gut transit or intestinal
microbiota for at least 3 mo before the start of the study. The study protocol
complied with the Helsinki Declaration and was approved by the Ethics
Committee of the University of Leuven. All subjects gave written informed
consent. Each of the subjects performed two tests, respectively with barley
porridge and barley kernels as test meal.
Study design
After an overnight fast, the volunteers presented in the morning at the laboratory
where they were asked to provide a basal urine sample and three breath
117
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
samples for measurement of respectively H2,
13
CO2 and
14
CO2. In both arms, a
catheter was introduced in an antecubital vein and a basal blood sample was
withdrawn. Consequently, the volunteers received a test meal prepared from 13Clabelled barley to which 74 kBq inulin-14C-carboxylic acid (Amersham
Biosciences, Uppsala, Sweden) (27) had been added as well as a glass of water.
The test meal was always consumed within 20 min. Blood samples were
collected from the contralateral arm every hour during the first 5 h, every 20 min
from 5 to 8 h and every 40 min from 8 up to 14 h. Breath samples were collected
every 20 min up to 6 h for analysis of 13CO2 and up to 10 h for analysis of H2 and
14
CO2. Three volunteers collected all urine for 24 h in different fractions using
specified receptacles to which neomycin was added for prevention of bacterial
growth. After 6 h, 9 h and 12 h, a standard, light and non-fermentable meal was
offered to the volunteers (sandwich with ham or cheese). Unlimited access to
water was allowed from 6 h after the start of the infusion.
Test meals
Production of labelled barley
Barley, Hordeum vulgare Var. Kirsty, was sown in 20 cm diameter pots each
containing 9 seeds. A proprietary growing medium was used (Shamrock Potting
Professional, Scotts, Ireland). The plants were grown in a greenhouse for
approximately 6 wk until anthesis, which was determined visually. For labelling
with
13
CO2, plants were moved in batches into a gas-tight 600 L growth cabinet
(Fisons Fi-totron) which was operated at 18 oC, with 16 h light per 24 h. The
cabinet contained a maximum of 18 pots.
from a gas cylinder (99 atom %
13
CO2 additions of 600 mL were made
13
C; Spectra Gases, NJ, USA) by transfer from a
fixed volume at known pressure. The plants were incubated for 4 d and then
returned to the greenhouse until harvesting approximately 3 mo later. The grain
was prepared and separated using a single ear thresher. Sub-samples of grain
were ground into fine flour. 13C abundance and carbon content was measured by
continuous-flow isotope ratio mass spectrometry (28).
118
13
C-enrichment was
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
calculated after subtracting the abundance of unlabelled grain grown under the
same conditions. Two batches of barley were produced. The first batch weighed
165 g and its
13
C enrichment was 1.852 atom %
weighed 171 g and its
13
C excess. The second batch
13
C enrichment was 2.985 atom %
13
C excess. The
average carbon content was 39.6 %.
Pearling/milling barley
A barley pearling machine was used to remove the outer hull of the barley
kernels. By this procedure the original weight of the barley kernels was reduced
by 15 %. For the porridge, barley kernels were milled in an electric household
grain mill (SAMAP Elsässer grain mill F 100) on the finest grade.
Preparation and composition of test meals
Barley porridge was prepared by addition of about 86 g of flour to 150 mL water
and gently blending to remove lumps. Thereafter, 300 mL additional water was
added along with 1 g of NaCl after which this portion was cooked in a microwave
oven for 5 min at 600 W.
For the barley kernel test meal about 86 g of barley kernels were boiled at low
temperature in 320 mL of water until all water was absorbed by the kernels
(approximately 40 min).
Analytical procedures
Analysis of breath samples
For
13
CO2 and hydrogen measurements, breath was collected in exetainers
(PDZ, Cheshire, UK). The
13
C-breath content was determined by isotope ratio
mass spectrometry (ABCA, PDZ, Cheshire, UK). We assumed CO2-production to
be 300 mmol/m² body surface area per hour (29). The body surface area was
calculated by the weight-height formula of Haycock et al (30). The results for
digestion of barley were expressed as the cumulative percentage of administered
dose of
13
C excreted over 6 h, the maximal excretion rate of
119
13
C and the time to
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
maximal excretion rate of
13
C. The amount of administered dose of
13
C was
calculated taking into account the known enrichment of the labelled barley and
the fact that an 86-g portion of barley contains 56 g of total starch.
Hydrogen was measured in an H2-monitor (MEC, Brussels, Belgium) which
contained an electrochemical cell and yielded a digital read out of hydrogen
concentration in parts per million (ppm).
Breath samples for measurement of
14
CO2 were collected by blowing through
a pipette into a vial containing 2 mmol of hyamine hydroxide until discoloration of
the thymolphtaleine indicator, corresponding to the capture of 2 mmol of CO2.
14
CO2 was measured by liquid scintillation counting (Packard Tricarb Liquid
Scintillation Spectrometer, model 3375, Packard Instruments Inc., Downers
Grove, IL, USA) after addition of 10 ml of Hionic fluor (Perkin Elmer, Boston,
USA). Orocecal transit time (OCTT, i.e. the time that elapses between intake of a
meal and arrival of the meal in the colon) was calculated on the basis of
14
CO2
and H2-excretion and was defined as the time at which a significant increase in
14
C and H2 from the background was seen, i.e. 2.5 times the standard deviation
of all previous points above the running average of all previous points (31).
Analysis of SCFA concentrations in plasma samples
Concentrations of SCFA were determined as previously described (32). Briefly,
plasma samples (500 µl) were deproteinised with sulfosalicylic acid after addition
of 3-methylvalerate as an internal standard. The supernatant fraction was
extracted with 3 ml diethyl ether and tert-butyl-dimethyl-silyl-imidazole was added
to the organic phase. The reaction mixture was heated at 60°C for 30 min,
cooled and evaporated to approximately 500 µl. SCFA derivatives were analysed
on a GC/MS (Hewlett Packard, Palo Alto, CA, USA) using positive chemical
ionisation.
Analysis of 13C-enrichment in plasma samples
120
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
After addition of 200 µL of NaCl (4M) (99.8% purity, Sigma-Aldrich) to 1 mL of
plasma, proteins were removed by centrifugation at 4575 g for 2 h 40 min using
Amicon centrifugal filter devices YM-30 (Millipore, Bedford, MA, USA). Plasma
ultrafiltrates were concentrated to 200 µL under a nitrogen stream and acidified
to pH 2.5-3 by adding 10 µL of HCl (2 M).
SPME fiber (CAR/PDMS from Supelco (Bellefonte, PA, USA)) was exposed
to the analytes by immersion in the sample at 40 °C for 45 min. The SCFAs
adsorbed on the SPME fiber were injected by direct desorption into a gas
chromatography (HP 5890, Hewlett Packard, Palo Alto, CA, USA). The
13
C-
SCFA analysis was performed using a gas chromatography - combustion isotope ratio mass spectrometry (GC/C/IRMS) (Delta S, Finnigan MAT, Bremen,
Germany) (25).
Calculation of the enrichment of 13C-SCFA
The data were obtained using the formula:
13
C(‰) = [(13C/12Csample - 13C/12CVPDB)/ 13C/12CVPDB]*1000
(where 13C/12CVPDB = 0.0112372),
therefore: 13C/12Csample = 13C/12CVPDB * [1+0.001*
The observed abundance of
13
C]
13
C in a sample, given in Atom Percent (AP) is
defined as:
AP = 100*13C/12CVPDB[(0.001
13
Csample)+1]
The isotopic enrichment in a sample as compared to a standard value (basal
value) is given in Atom Percent Excess (APE):
APE = APsample – APbasal value
Finally, plasma concentrations of
13
C-SCFA were calculated by multiplying the
APE of the respective SCFA with its plasma concentration.
Analysis of urine samples
Urine samples were stored at -20 oC until analysis. SCFA were extracted from
2.5 mL samples following ultrafiltration to remove high molecular weight
121
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
compounds and solid phase extraction to concentrate and purify samples.
Samples were stored dry with excess alkali. They were acidified and back
extracted into ether prior to analysis. Concentration analysis and standardisation
of
13
C abundance were achieved by use of a 3-methyl valerate internal standard
(33).
13
C abundance was measured by GC-C-IRMS and
13
C enrichment of
urinary SCFA was calculated as above. SCFAs were measured by GC/MS as
described by Morrison et al (33).
In vitro analyses of starch fractions
An adapted version of the method described by Englyst (34) was used to
analyse the starch fractions in the test meals. In short, samples were
disintegrated, if necessary, in order to mimic chewing and incubated with pepsinHCL. After adjustment of the pH, samples were incubated with pancreatin and
microbial amyloglucosidase for 2 h. Samples were taken after 20 and 120 min of
incubation time. The amount of glucose present after 20 min of incubation
presented the rapidly available glucose fraction. After 120 min total digestible
glucose was measured (G120). The amount produced between 20 and 120 min
of incubation presented the slowly available glucose fraction. After 120 min the
samples were treated with sodium hydroxide in order to solubilise resistant
starch. This resistant starch was converted quantitatively into glucose and total
glucose (TG) was measured. The resistant starch fraction was calculated by
subtracting G120 from TG and by multiplying the result by 0.9.
Statistical analysis
Results are expressed as mean values and standard deviations. Non-parametric
statistical evaluation (Wilcoxon test and Mann-Witney test) was performed using
SPSS software (SPSS 15.0 for Windows; SPSS Inc., Chicago, IL, USA). The
level for statistical significance was set at P < 0.05. Differences for which Pvalues were <0.1 were considered as tendencies.
122
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
Results
In vitro analysis of starch fractions
Total starch content and starch fractions of the test meals were analysed in vitro.
The total starch content in the barley meals was 56 g. The percentages of rapidly
available glucose, slowly available glucose and resistant starch of total starch in
the barley kernels and barley porridge were 62, 34, 4 and 98, 2, 0 respectively.
Soluble fiber content of barley is reported to lay between 8 and 14 % (dry weight)
(35).
OCTT
OCTT was calculated on the basis of both an increase in H2-excretion, as an
indication of fermentation of the administered barley, and an increase in
14
CO2-
14
excretion, indicating hydrolysis of the tracer inulin- C-carboxylic acid added to
the test meal. Figure 1 shows a representative example of the excretion of H2
and
14
CO2 obtained after administration of a barley porridge meal to a volunteer.
Although OCTT values obtained on the basis of H2-excretion were statistically
significantly shorter than values obtained on the basis of 14CO2 measurements (P
= 0.023), the mean difference between both measurements was only 20 min
which corresponds to one sampling interval. Hence, this difference was not taken
into account.
No statistically significant differences were observed in OCTT values of
barley porridge (360 ± 47 min for H2-measurements and 365 ± 55 min for
14
CO2-
measurements) as compared to barley kernels (372 ± 18 min for H2measurements and 400 ± 0min for
14
CO2-measurements)(P = 0.705 for H2-
measurements and P = 0.285 for 14CO2-measurements).
123
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
120
30
100
25
80
20
60
15
40
10
20
5
0
0
100
200
300
400
500
CO2-excretion (dpm)
35
14
H2-excretion (ppm)
Figure 1 Representative example of H2-excretion and 14CO2-excretion obtained
after administration of a barley porridge test meal, to which 74 kBq
inulin-14C-carboxylic acid had been added
0
700
600
tim e (m in)
H2
14CO2
Digestion of administered 13C-barley
The parameters reflecting the extent of digestion of the
presented in Table 1. The cumulative
were not significantly different for
13
C-barley test meals are
13
C-excretion as well as the peak excretion
13
C-barley porridge and for
13
C-barley kernels,
suggesting no difference in terms of the ultimate quantity of tracer oxidised
between the porridge meal and the kernel meal. The time to maximal
13
C-
excretion was significantly shorter for the porridge meal (P = 0.043) indicating a
faster digestion than that of the kernel meal.
124
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
Table 1 Small intestinal digestion of administered 13C-barley meals (mean ± sd)
13
C-barley porridge
Cum
13
C-barley kernels
13
C-excretion
(% of administered dose)
peak excretion of
33.9 ± 19.5
26.7 ± 15.1
7.9 ± 4.9
6.3 ± 3.6
200 ± 32
300 ± 42*
13
C
(% of administered
dose/h)
time to peak excretion of
13
C (min)
* significantly different from barley porridge (P < 0.05)
13
C-SCFA concentrations in plasma
13
C-SCFA, the main products from colonic
The plasma concentration of
fermentation of administered
13
C-barley are presented in Figure 2.
After administration of both test meals, an increase in
13
C-acetate was
already observed in the early phase (< 360 min) (Figure 2, A). A second peak in
13
C-acetate enrichment was observed in the later phase (> 360 min) and was
higher after the barley kernels test meal than after the barley porridge test meal.
The area under the curve (AUC) tended to be higher after barley kernels.
However, the difference did not reach statistical significance (P=0.064). A rise in
13
C-propionate enrichment was detected after the barley porridge meal for only 3
volunteers but never in the plasma after the barley kernels meal (Figure 2, B). An
increase of
13
C-butyrate enrichments was only found in the fermentation phase
and was comparable after the barley kernels and barley porridge meal (Figure 2,
C). Consequently, AUC for
13
C-butyrate were not different for both meals
(P=0.65).
The proportion of
13
C-acetate of total
13
C-SCFA in plasma was 99% after barley
kernels and 98.3% after barley porridge.
125
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
Figure 2 Mean plasma enrichments 13C-acetate (A),
butyrate (C) after a barley kernels (
) test meal (n = 5 subjects; mean ± sd)
A
126
13
C-propionate (B) and 13C) and barley porridge (
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
B
127
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
C
Excretion of 13C-SCFA in urine
Figure 3 (A-C) shows the cumulative urinary excretion of the respective
SCFA. Cumulative
13
C-
13
C-acetate excretion after 24 h was similar after both meals
(P=0.57). However, after barley porridge,
13
C-acetate excretion proceeded
somewhat faster than after barley kernels, which is in agreement with the higher
13
C-acetate plasma concentrations in the early phase. Unlike in plasma,
13
C-
propionate was retrieved in urine after both meals although in lower
concentrations after barley kernels. A tendency towards a significantly higher
cumulative excretion after barley porridge than after barley kernels was observed
128
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
(P=0.098). The cumulative excretion of
13
C-butyrate was borderline significantly
higher after barley kernels (P=0.057).
Also in urine,
of total
13
C-acetate was by far the most predominant
13
C-SCFA (97.4%
13
C-SCFA after barley kernels and 93.5% after barley porridge).
Discussion
In vitro fermentation studies using human (36-38) or swine inocula (39) have
shown that the amounts and the pattern of SCFA production vary among
different types of fermentable carbohydrate. For example, hydrolyzed guar gum
produced the greatest amount of total SCFA and was involved in high production
of propionate and butyrate whereas psyllium husk was involved in high
production of butyrate. Similarly, the type of non-digestible carbohydrate
consumed was found to influence the distribution of SCFA in the hindgut of rats
(40). Rats fed pectin had a high proportion of acetic acid in the cecum whereas
those fed guar gum had a higher proportion of propionic acid. In addition,
combination of carbohydrates drastically changed the SCFA profile.
Due to the inaccessibility of the colon in healthy, free-living humans it is
extremely difficult to estimate the production profile of SCFA in the colon and
their bioavailability to the host in humans. Faecal concentrations of SCFA do not
adequately reflect their production because of the high absorption in the colon.
Stable isotope tracer techniques with isotope dilution have been suggested as a
way to estimate the colonic acetate production (41) and the whole body acetate
flux (33). Ahmed et al. studied colonic SCFA formation in patients with left-sided
colectomy and found a higher SCFA production and in particular butyrate
production after a high-resistant starch diet as compared to a low-resistant starch
diet. As yet, no data is available on the bioavailability of SCFA in the systemic
circulation.
In the present study, we have developed a new strategy in which plasma and
urine concentrations of
13
C-labelled acetate, propionate and butyrate were
129
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
measured after administration of two stable isotopically labelled carbohydrate
test meals, barley porridge and barley kernels. Barley porridge is rich in NSP
whereas barley kernels contain resistant starch in addition to NSP. The
13
CO2-
excretion data showed that the porridge test meal was digested faster as
compared to the kernel test meal (shorter time to peak excretion) which is in
agreement with the higher amount of rapidly available glucose in the porridge
meal as revealed from the in vitro starch fractions analysis. In contrast, the
extent of digestion and fermentation was relatively similar for both test meals
(similar cumulative
13
C-excretion). The amount of substrate reaching the colon
may have been similar in both occasions; however, the profile of enriched SCFA
in plasma differed depending on the test meal administered. Whereas only
acetate and
actate,
13
C-butyrate were found after administration of barley kernels,
13
C-propionate and
13
C-
13
C-
13
C-butyrate were observed after administration of
barley porridge. Based on several in vitro and in vivo studiessuggesting that
resistant starches are good sources for butyrate production (38;42-44), we
assume that colonic
13
C-butyrate production was higher after the barley kernels
meal than after the porridge meal. Nevertheless, plasma
concentrations were rather similar after both meals.
observed in urinary
13
C-butyrate
A similar pattern was
13
C-butyrate excretion up to 12 h. However, urinary
13
C-
butyrate excretion from 12 to 24h tended to be higher after barley kernels than
after barley porridge (P=0.74). These results suggest that butyrate production in
the colon prodeeds slower than acetate or propionate production. This might be
explained by the fact that butyrate is not produced directly from the fermented
carbohydrate but through conversion of acetate (so-called mechanism of crossfeeding)(45;46).
In vitro studies have shown that fermentation of barley derived β-glucans by
human
faecal
microbiota
yields
SCFA
in
a
molar
ratio
of
acetate:propionate:butyrate of 51:32:17 (47), which is considered propionaterich. However, the
13
C-SCFA were retrieved in plasma as well as in urine in a
130
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
molar
131
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
Figure 3 Mean urinary enrichments of 13C-acetate (A), 13C-propionate (B) and
13
C-butyrate (C) after a barley kernels and barley porridge test meal
(n=3 subjects; mean ± sd)
A
B
132
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
C
ratio of acetate:propionate:butyrate of 98:1:1, reflecting differential absorption
and metabolism in colonic epithelial cells and liver.
Data on
13
C-enrichment of SCFA in urine confirmed the observed difference
in SCFA profile after the two test meals. Interestingly,
13
C-enriched propionate
was also observed in urine after the barley kernel test meal although to a lower
extent than after the porridge meal. The measurement of low
involves subtraction of basal
13
C enrichment
13
C abundance from each analysis. At low SCFA
concentrations (as occurs with propionate and butyrate) this results in larger
errors in estimating enrichment. However, the generally good agreement
between plasma and urinary data, which were analysed in different laboratories,
suggests that analysis of urinary SCFA enrichment profiles provides an adequate
and non-invasive way to monitor the availability of SCFA to the host. This
observation confirms previous data in which a similar whole body acetate flux
was obtained from measurement of urine or plasma tracer enrichments (33).
The orocecal transit time of both test meals was measured to allow
correlation of changes in plasma
13
C-SCFA concentrations to the digestion
phase (before OCTT) or the fermentation phase (after OCTT). OCTT values
were similar for both test meals and amounted to approximately 6h.
133
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
When looking at the kinetics of appearance of
13
circulation, it was found that plasma concentration of
C-labelled SCFA in the
13
C-acetate was already
increased as early as after 3h, i.e. during the digestion phase (<6h). This
observation could suggest that
13
C-acetate is derived from digestion of barley
starch to glucose which undergoes glycolysis. However, during glycolysis,
glucose is converted to pyruvate which, in aerobic conditions, enters the cell’s
mitochondrion to be further oxidised to Acetyl-CoA which enters the Citric acid
cycle. It is rather unlikely that free
13
C-acetate is formed during this process and
Acetyl-CoA is not transported across the mitochondrial membrane. Alternatively,
13
C-acetate could be derived from early bacterial degradation of the test meal, a
hypothesis which is favoured by the concomitant appearance of labelled
propionate and butyrate, albeit to a lesser extent. All labelled SCFA were found
in the 0-6h urine fraction as well, contributing to the hypothesis of early
fermentation. It is not clear whether this early fermentation should be due to an
easily fermentable compound of the test meal, to rapid transit of the head of the
meal or to fermentation in the small intestine where, in the more distal parts, the
density of bacterial populations lies between 104 to 108 cells/ml (as compared to
1012 cells/ml in the colon) (48).
The methodology used in the present study allowed qualitative description of
the SCFA reaching the circulation after consumption of a test meal rich in nondigestible carbohydrates. To be able to relate colonic fermentation and SCFA
availability to the effects of SCFA on peripheral tissues, it will be necessary to
obtain quantitative results. Therefore, the methodology needs to be further
adapted, probably by in vivo application of known quantities of isotope-labelled
SCFA.
In conclusion, the method described in this study allows to evaluate SCFA
profiles obtained after administration of different non-digestible carbohydrates in
healthy humans. The results of the present study have clearly shown that meals
containing dietary fiber combined with resistant starch result in altered SCFA
profiles as compared to meals containing dietary fiber alone.
134
APPENDIX 4. TYPE OF CARBOHYDRATES AND SCFA PROFILES
Acknowledgement
This work was financially supported by the Commission of the European
Communities, and specifically the RTD programme ’Quality of Life and
Management of Living Resources’, QLK 1-2001-00431 ’Stable isotope
applications to monitor starch digestion and fermentation for the development of
functional foods’ (EUROSTARCH). This work does not necessarily reflect its
views and in no way anticipates the Commission’s future policy in this area.
Further financial support was obtained from the Fund for Scientific ResearchFlanders (F.W.O.-Vlaanderen, Belgium).
We thank D. Binnema and P. Sanders from TNO, Quality of Life, Groningen,
The Netherlands for in vitro starch analysis of the test meals.
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140
SAMENVATTING
SAMENVATTING
De consumptie van koolhydraten is gerelateerd aan de preventie van type 2
diabetes. Twee belangrijke fysiologische processen spelen hierbij een rol: a.
vertering in de dunne darm: de verlaging van de postprandiale glucose respons
(glucose respons na de maaltijd) en b. de fermentatie van niet-verteerbare
koolhydraten in de dikke darm.
Verondersteld wordt dat het fermentatie proces een rol speelt middels de
korte keten vetzuren (kkv, met name acetaat, propionaat en butyraat).
Uit onze experimenten bleek dat gezonde mannelijke vrijwilligers die een
avondmaaltijd met niet-verteerbare koolhydraten (gerstkorrels) hebben genuttigd,
de volgende morgen (second meal effect) een verlaagde post-prandiale glucose
respons hadden. Na de controle avondmaaltijd (wit brood) trad ’s ochtends een
door glucose geïnduceerde toename van de ontstekingsfactoren interleukin 6
(IL6) en tumor necrosis factor (TNF)- α op. Deze werd echter niet waargenomen
na de gerst-avondmaaltijd. De toegenomen waterstof productie en de verhoogde
plasma butyraat spiegel suggereren sterk dat fermentatie in de dikke darm
verantwoordelijk is voor deze gunstige eigenschappen met butyraat als
intermediaire factor. (Hoofdstuk 1)
Omdat een afgenomen insuline gevoeligheid en toename van proinflammatoire cytokines geassocieerd zijn met de ontwikkeling van diabetes type
2 en hart- en vaatziekten, suggereren onze resultaten dat fermentatie in de dikke
darm belangrijk kan zijn voor de preventie van deze chronische ziektes en dit
beschermend effect in het bijzonder optreedt bij niet-verteerbare koolhydraten
met een speciaal kkv profiel.
De literatuur is samengevat betreffende het belang van kkv voor insuline
gevoeligheid en de mogelijkheid de fermentatie in de dikke darm zo te
veranderen dat een optimaal kkv profiel wordt verkregen.
Fermentatie
in
de
dikke
darm
kan
beïnvloed
worden
door
voedingsveranderingen, veranderingen van de passage tijden van dunne en
dikke darm, toepassing van remmers van dunne darm hydrolyse enzymen en
141
SAMENVATTING
beïnvloeding van de activiteit en specificiteit van dikke darm bacteriën. Onze
hypothese is dat de verhoogde insuline gevoeligheid gerelateerd is aan het kkv
profiel. Verandering van het kkv profiel kan dus een target zijn om via voeding de
insuline gevoeligheid te beïnvloeden en hetgeen preventief kan zijn voor de
daaraan gerelateerde chronische ziekten. De functional foods die een kkv profiel
produceren dat gelijk is aan dat van gerstkorrels, zouden het vermogen hebben
voor de verbetering van insuline gevoeligheid en preventie diabetes type 2.
(Hoofdstuk 2)
Het metabole effect van koolhydraten wordt bepaald door de mate waarin
koolhydraten worden verteerd in de dunne darm en gefermenteerd in de dikke
darm. Wij hebben een wiskundig model ontwikkeld om in vivo in de mens de
vertering en fermentatie van koolhydraatrijke voedingen te bepalen.
Dit model verschaft ons op een niet- invasieve manier een methode om de
bijdrage van zowel vertering als fermentatie aan het totale metabole effect in
kaart te brengen. Door gebruik te maken van 13C gemerkte voedingen kunnen
wij een fermentatie index voor elke koolhydraatrijke voeding bepalen. Deze
fermentatie index kan samen met de glycaemische index op een beknopte
manier
informatie
verschaffen
over
de
metabole
karakteristieken
van
koolhydraatrijke voeding. (Hoofdstuk 3)
De fermentatie van verschillende niet-verteerbare koolhydraten inclusief
voedingsvezels is tot op heden niet goed beschreven. Om dit te bereiken is een
analytische methode ontwikkeld om het kkv profiel in het perifeer bloed en urine
te kunnen meten na inname van niet verteerbare koolhydraten. Wij gebruikten
deze methode om na te gaan hoe het kkv profiel wordt beïnvloed door
koolhydraat rijke voedingen. De testvoeding was gemerkt met stabiele isotopen
waardoor wij de relatie konden leggen tussen de fermentatie producten en de te
onderzoeken voeding. Wij vonden dat kkv profielen in plasma en urine konden
worden veranderd door de verschillende niet verteerbare koolhydraten.
(Hoofdstuk 4)
142
SAMENVATTING
Ter conclusie, met deze nieuwe technieken om de fermentatie in de dikke
darm te analyseren waren wij in staat het metabolisme van niet-verteerbare
koolhydraten te onderzoeken in relatie met de verbetering van insuline
gevoeligheid en preventie van type 2 diabetes.
De relatie tussen fermentatie in de dikke darm en insuline gevoeligheid wordt
door steeds meer gegevens ondersteund. Verandering van fermentatie van
koolhydraten in de dikke darm lijkt een veelbelovende aanpak te zijn om de
insuline gevoeligheid en de daaraan gerelateerde ziekten positief te beïnvloeden.
Het design van een zg. ``second meal`` studie is een van de experimentele
benaderingen om de gunstige effecten van voedingen rijk aan niet-verteerbare
koolhydraten in kaart te brengen.
143
ACKNOWLEDGEMENTS
ACKNOWLEDGEMENTS
The Netherlands is such a fabulous country that I am surprised to realize that I
have been there for more than three years. I have a peaceful, easy, and happy
life in Groningen. At the same time, obtaining a PhD in three year is a big
challenge for me, although I have already got a PhD in China. However, this
thesis is finally finished. It is a great success, which deserves a big celebration.
It’s an achievement of all the people who has been supporting and concerning
me. I thank all of you.
Thanks to my family. I am luck to have a big family. No matter how far away I
am from my home town, you are always with me. I am flying in the sky like a kite,
with the cord held by you. You provide me with confidence and direction. Your
love, support and concern are the endless sources of my courage and strength.
Special thanks to my grandparents of four. Grandfather from mother side, you
taught me to tolerate and cherish. Grandmother from mother side, you taught me
to love and dedicate. Grandfather from father side, you gave me courage,
knowledge and dream. Grandmother from father side, you never left our village
beyond 50 km all your life, but you saw through all this world with your insistence.
My grandparents, you watched, guided and protected me to grow up. You saw
my leave, but you are not able to wait to welcome me home and see my success.
And I never got any chance to listen to your last word. However, I feel you are
always somewhere with me, including this moment.
Special thanks to my first teacher, Mr. Ye. It’s you who enlightened me and
changed my life. You had been proud of me. You were sick when I visited you
early last year. You said you might never see me again, and you left just two
days before I went back to see you again half year later. How I wish you are still
here to be proud of me!
Special thanks to my little son. You bring me great pleasure and make me a
real man. I should have been accompanying you and witnessing your growth. It’s
144
ACKNOWLEDGEMENTS
a pity I am not able to be with you when you need me. I love you forever. I wish
you all the best.
Thanks to He Tao, Erwin, Fu Jingyuan, Yang Haifei, Wang Ying, Zhou Lu,
Zhang Jianhong, Zhou Chaohong, Mateusz and Kasia who take care of me like
my brothers and sisters. You all have cute and healthy children who give me a
great pleasure of life. I wish them a bright future and a happy life!
Thanks to my promotor, Professor Vonk. You brought me to Groningen and
led me to a wonderful scientific world. You are an excellent guider for both my
research and my life. As a Dutch, you provide me an opportunity to experience
the traditional Chinese teacher-student relationship, the relationship between
father and son.
Thanks to my supervisor, Marion Priebe. It’s my honour to work with you and
to share the same office. This thesis is the outcome of our excellent cooperation.
Your passion for science, your dedication to work, and your precise way of doing
job make you a great role model for me. This thesis can not be made without
your full support.
Thanks to Tom and Sandra. You took good care of me and provided me with
excellent training when I was in Glasgow. Without the expertise I learnt from you,
it would not be possible for me to finish this thesis.
Thanks to all my colleagues in UMCG. You make me feel at home in UMCG.
The wonderful time we have had together is unforgettable. I had already started
to miss you before I left Groningen.
Thanks to Professor Yang Yuexin. You introduced me to nutritional science.
It’s my great honor to be your student. Once my mentor, you are my mentor
forever. Thanks for your long-standing support and concern.
Thanks to my friends in the United States, Fran Seligson, Lu Zhenhua, Wang
Wenjuan and Ouyang Zengyuan, your concern and support have been with me
across the Atlantic Ocean, or the Pacific Ocean, wherever I am.
145
ACKNOWLEDGEMENTS
Thanks to Zhang Hao, Zhang Junhao, Hou Dun and other team member of
our football team. Enjoying football is a wonderful part of my life in Groningen.
You made it happen.
Thanks to Martijn, Marten, Marijan, Klaas, Jolen and other team member of
Groningen student table tennis club. We had training together and played
tournament together. The wonderful time we had together has been vivid in my
mind.
Thanks to my friends in Groningen, Gjalt, Sytske, Pieter-Jan, Wang Hao, Ren
Yijin, Jin Lihua, Qin Li, Qin Si, Lu Wenli, Qi Liqiang, Yang Huiqi, Xiao Danna,
Zhao Yingru, Wu Bian, Ma Yue, Yang Nan, Yi Xia, Wei Yunwei, Xu Chuanhui, Yu
Ziling, Xue Ruiqi, Liu Bo, Qian Cheng, Lv Bo, Yin Meimei, Wu Ting, Miao Yan,
Yang Xiaomei, Tan Lijun, Tan Hongtao, Ye Qingsong, Ding Ning, Xiao Jingfei, Li
Qian, Liu An, Arjen, Carmen, and Margien.
Thanks to the committee of Foreign Guest Club. You always dedicate to
organize wonderful and considerate activities for our foreigners. This has
become an unforgettable part of my life in Groningen.
Special thanks to Zhu Lilin. In an exclusive way, you let me know how to love
and to be loved; you reminded me what responsibility is for a man; you taught
me how to face any fortune and misfortune in life. God bless you!
Thanks to all my relatives and friends who have been caring and supporting
me!
My study in Groningen was closed and I come back to China, Thanks to
Professor Lin xu for providing me a job opportunity in your great research group.
And I thank Professor Chen Junshi, Professor Yang Yuexin and Dr. Han Fanfan
who made this happen.
With what I get from all of you, I start my new life in Shanghai.
146
CV
CURRICULUM VITAE
Hongwei Wang was born on 10 December 1975 (traditional Chinese calendar) in
a village in Jiangchuan, Yunnan, China.
After 5 years of primary school study in his village, 3 years of middle school
study in Jiangchuan No. 1 middle school, and another 3 years of high school
study in Yuxi No.1 high school, he was admitted to Beijing Medical University in
1994, majoring in preventive medicine. He continued his master study in Beijing
Medical University in 1999, majoring in molecular genetic toxicology, supervised
by Professor Hao Weidong. In 2003, he became a PhD student of Professor
Yang Yuexin at the Institute of Nutrition and Food Safety, Chinese Center for
Diseases Control and Prevention. He obtained his PhD degree in nutrition and
food hygiene in 2006. He moved to Groningen and was recruited to pursue
another PhD at University Medical Center Groningen.
Since 2003, he has been studying the relationship between dietary nutrients
and chronic diseases, especially, the relationship between non-digestible
carbohydrates and type 2 diabetes. He is focusing on the health effects of
colonic metabolism and short-chain fatty acids production of non-digestible
carbohydrates. This part of work is concluded as his thesis in the Netherlands.
He has got a job at the Institute for Nutritional Sciences, Shanghai Institute for
Biological Sciences, Chinese Academy of Science. He will work as an associate
professor to continue his research on nutrition and chronic diseases.
147
LIST OF PUBLICATIONS
LIST OF PUBLICATIONS
Wang H, Priebe MG, Vonk RJ. Manipulation of colonic short-chain
fatty acid production: relevance for the prevention of insulin resistance.
In process.
Priebe MG, Wang H, Weening D, Schepers M, Preston T, Vonk RJ.
Factors related to colonic fermentation of non-digestible carbohydrates
of a previous evening meal increases tissue glucose uptake and
moderates glucose-associated inflammation. Am J Clin Nutr
2010;91:90-7.
Verbeke K, Ferchaud-Roucher V, Preston T, Small AC, Henckaerts L,
Wang H, Vonk RJ, Priebe MG. Type of indigestible carbohydrate
affects plasma and urine short chain fatty acid profiles in healthy
human volunteers. Eur J Clin Nutr 2010;64:678-84.
Wang H, Weening D, Jonkers E, Boer T, Stellaard F, Small AC,
Preston T, Vonk RJ, Priebe MG. A curve fitting approach to estimate
the extent of fermentation of indigestible carbohydrates. Eur J Clin
Invest 2008;38:863-868.
Wang H, Han J, Zhang W, He M, Yang J, Yang Y. The influences of
resistant starch on insulin resistance in rats. Acta Nutrimenta Sinica
2007;29: 131-133.
Wang H, Yang Y, Zhang Y, Han J, Wang Z. Acceptance of knowledge
to food glycemic index and dietary adjustment in diabetic patients.
Journal of Clinical Rehabilitative Tissue Engineering Research
2007;11:10701-03.
Zhang W, Wang H, Zhang Y, Yang Y. Effects of resistant starch on
insulin resistance of type 2 diabetes mellitus patients. Chinese Journal
of Preventive Medicine 2007;41:101-104.
Wang H, Yang Y. The factors and regulation of postprandial blood
glucose response. Journal of Hygiene Research 2006;35:234-237.
148
LIST OF PUBLICATIONS
Yang Y, Wang H, Cui H, Wang Y, Xiang S, Zhou S, Yu L. Glycemic
index of cereals and tubers produced in China. World Journal of
Gastroenterology 2006;12:3430-3433.
Wang H, Shang L, Hao W. The induction of apoptosis and reactive
oxygen species in human skin fibroblast by ultraviolet A. J Peking Univ
[Health Sci] 2002;34:69-73.
Hao W, Shang L, Zhao H, Wang H, Lu J. Effect of DNA oxidative
damage on the mutagenesis of M13mp2 phage by sunlight. China
Environmental Science 2000;20:154~158.
149
ABBREVIATIONS
ABBREVIATIONS
A
acarbose
AB
gum A. baileyana
ACHO
AP
dose of avilable carbohydrate
adenosine 5'-monophosphate activated protein
kinase
atom percent
AP
gum A. pycnantha
APE
atom percent excess
AUC
area under the curve
BA
barley kernels
BCFAs
branch-chain fatty acids
BSA
body surface area
CB
cabbage fiber
CS
corn starch
DF
dietary fiber
EGP
exogenous glucose production
FFA
free fatty acids
GA
gum arabic
GC
GC/MS
gas chromatography
gas chromatorgraphy/combustion/isotope ratio mass
spectrometry
gas chromatography/mass spectrometry
GCR
glucose clearance rate
GI
glycemic index
GLUT4
glucose transporter 4
GPR41
the orphan G protein-coupled receptor
AMPK
GC/C/IRMS
150
ABBREVIATIONS
iAUC
the incremental area under the curve
IL 6
interleukin 6
ISI
insulin sensitivity index
LPS
lipopolysaccharide
MCR
metabolic glucose clearance rate
mRNA
messenger ribonucleic acid
NF-κB
nuclear factor kappa B
NSP
non-starch polysaccharides
OCTT
oral-cecal transit time
OGTT
oral glucose tolerance test
OOPSEG
the optimized optimal segments
PAL
physical activity level
PBMC
peripheral blood mononuclear cells
ppm
parts per million
RaE
systemic rate of appearance of exogenous glucose
RaT
systemic rate of appearance of total glucose
RS
resistant starch
SCFAs
short-chain fatty acids
SDF
soluble dietary fiber
sub
subject
T2DM
TG
type 2 diabetes
dose of total carbohydrates available for digestion
and fermentation
total glucose
TNF-α
tumor necrosis factor α
WB
white bread
TCHO
151