Glucose Homeostasis and Insulin Resistance in veal calves
Pantophlet, Andre Jonatan
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Pantophlet, A. J. (2018). Glucose Homeostasis and Insulin Resistance in veal calves: Studies on the effects of age, nutritional modulations and the applicability of metabolic profiling techniques.
Rijksuniversiteit Groningen.
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Insulin Resistance in veal calves
Studies on the effects of age, nutritional modulations
and the applicability of metabolic profiling techniques
Carbohydrate Competence Center (CCC2 WP21), and was part of a multidisciplinary collaboration between the VanDrie Group, Tereos Starch & Sweeteners Europe, Wageningen University and the University Medical Center Groningen.
The publication of this thesis was financially supported by the University of Groningen (RUG), Graduate School of Medical Sciences (GSMS) and the University Medical Center Groningen (UMCG).
ISBN: 978-94-6375-181-0
Cover and layout design: Iliana Boshoven-Gkini | www.AgileColor.com Printed by: Ridderprint | www.ridderprint.nl
Copyright © 2018 by Andre Jonatan Pantophlet. All rights reserved. No part of this thesis may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without permission of the author and when appropriate, the publisher holding the copyrights of the published articles.
in veal calves
Studies on the effects of age, nutritional modulations and the
applicability of metabolic profiling techniques
Proefschrift
ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op Maandag 19 november 2018 om 12.45 uur
door
Andre Jonatan Pantophlet
geboren op 26 februari 1986 te Willemstad, Curaçao
Prof. dr. R.J. Vonk
Copromotores
Dr. M.G. Priebe
Dr. J.J.G.C. van den Borne
Beoordelingscommissie
Prof. dr. G. van Dijk Prof. dr. L. Dijkhuizen Prof. dr. J. Glatz
CHAPTER 1
General Introduction
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CHAPTER 2
Substantial replacement of lactose with fat in a
high-lactose milk replacer diet increases liver fat accumulation but does not affect insulin sensitivity in veal calves
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CHAPTER 3:
The use of metabolic profiling to identify insulin resistance in veal calves
47
CHAPTER 4:
Lactose in milk replacer can partly be replaced by
glucose, fructose, or glycerol without affecting insulin sensitivity in veal calves
63
CHAPTER 5:
Insulin sensitivity in calves decreases substantially during the first 3 months of life and is unaffected by weaning or fructo-oligosaccharide Supplementation
81
CHAPTER 6:
Short communication: Supplementation of
fructo-oligosaccharides does not improve insulin sensitivity in heavy veal calves fed different sources of carbohydrates
99
CHAPTER 7:
Discussion and Conclusions
107
Summary 117
Samenvatting 121
List of peer reviewed publications 125
Curriculum Vitae 127
Acknowledgements 129
CHAPTER 1
General Introduction
“Research is creating new knowledge.”
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1.1 PhD project outline
Insulin resistance is a key factor in the development of type 2 diabetes, which has become a major health issue worldwide. Problems with insulin resistance and glucose metabolism have not only been observed in human, but also in various animal species including cats, dogs, horses and calves. Studies in these animals can provide important information, which may also be applied in the human situation. Several genetic and environmental factors, such as diet, aging and stress, have been implicated with the development of insulin resistance. Modulations in the diet could therefore potentially prevent the development of insulin resistance and type 2 diabetes.
Milk-fed calves raised for white veal production (i.e. veal calves) are fed a milk replacer (MR), roughage and concentrates. The vast majority of the digestible nutrient
intake (60-70%) originates from the MR, which contains high amounts of lactose and fat (~45% and 35% of the digestible energy intake, respectively). Heavy veal calves (> 4 months of age) often develop problems with glucose homeostasis (Hostettler-Allen et al., 1994; Hugi et al., 1997), which lead to urinary glucose excretion (Vicari et al., 2008; Labussiere et al., 2009b) and hepatic steatosis (Gerrits et al., 2008), and could possibly develop into pre-diabetes. This might be attributed to the persistently high intakes of lactose from the MR (Hugi et al., 1998). Replacement of the lactose in the MR by other energy sources may improve glucose homeostasis and insulin resistance, which may stimulate efficient use of nutrients for growth processes and improve (metabolic) health. In addition, the price of lactose is subject to extremely large fluctuations, therefore providing also an economic incentive to replace lactose by alternative energy sources. Furthermore, studies in calves may increase our insights in the underlying mechanisms involved in the development of insulin resistance.
Therefore, in this project, the main aims were: 1) to increase the understanding of the pathology of insulin resistance in veal calves and to enable the development of feeding strategies to reduce the incidence of insulin resistance, and 2) to find a suitable substitution for a substantial part of the lactose in MR diets, without compromising nutrient digestion/fermentation and metabolic health in veal calves. The current PhD thesis focuses on the first aim. A second thesis has been devoted to the second aim (Gilbert, 2015).
The project was conducted within the framework of the Dutch Carbohydrate Competence Center (CCC), and was part of a multidisciplinary collaboration between the VanDrie Group, Tereos Starch & Sweeteners Europe, Wageningen University and the University Medical Center Groningen.
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1.2 Glucose metabolism and insulin resistance
1.2.1 Glucose homeostasis
Glucose is one of the most important energy sources for the body. The homeostasis of blood glucose is a complex process involving several tissues and receptors, hormones and co-regulators (Barros et al., 2009). In the fasted state, endogenous glucose production (predominately by the liver) ensures that sufficient glucose is available for body tissues and especially the brain. In the fed state, glucose is mainly derived from exogenous sources. Glucose homeostasis is maintained by pancreatic insulin, specifically produced by the islets of Langerhans. The blood glucose level stimulates pancreatic β-cells to secrete insulin. When blood glucose levels rises, insulin is secreted to stimulate glucose uptake primarily in skeletal muscle and adipose tissues. Also, insulin promotes glycogenesis (storage of glucose as glycogen) and inhibits glucose production via glycogenolysis (breakdown of glycogen to glucose) and gluconeogenesis (biosynthesis of glucose from non-carbohydrate precursors). In addition, insulin also promotes de novo lipogenesis from glucose (Stanfield, 2012; Röder et al., 2016). The actions of insulin on different tissues are summarized in Figure 1.1.
When blood glucose levels drop below a certain level, glucagon, a counter-regulatory hormone to insulin produced by pancreatic α-cells, is secreted to promote glycogenolysis and gluconeogenesis, to help maintain glucose homeostasis (Röder et al., 2016).
Several other hormones, such as the incretin hormones glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1), amylin,
adrenalin, cortisol, leptin, adiponectin, insulin-like growth factor and growth hormone, are known to be involved in the regulation of glucose homeostasis (Sherwin et al., 1980; Denroche et al., 2012; Eelderink et al., 2012a; Eelderink et al., 2012b; Hayes et al., 2014; Pantophlet et al., 2016). This illustrates the complexity of glucose homeostasis.
1.2.2 Insulin signaling pathways for glucose transport stimulation
One of the most important actions of insulin is the stimulation of glucose uptake, which occurs through a cascade of signaling events (Figure 1.2). Initially, insulin binds to the insulin receptor, a protein embedded in the cell membrane consisting of two extracellular α subunits and two transmembrane β subunits (Kanzaki, 2006). Binding induces a conformational change that activates tyrosine kinase activity of the β subunits. At least two discrete intracellular signaling pathways have been identified (Bryant et al., 2002; Watson et al., 2004; Kanzaki, 2006).
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Figure 1.1 | Actions of insulin on target tissues. Figure adapted from (Stanfield, 2012).
At first, a number of intracellular proteins, which include insulin receptor substrates (IRSs), growth factor receptor-bound protein 2 associated-binding protein 1 (GAB1)
and Shc are phosphorylated (activated), providing binding sites for downstream signaling molecules. Among them, phosphoinositide 3-kinase (PI3K) has a major role
because it activates Protein kinase B (PKB; also known as AKT) and Protein kinase
C (PKC), which leads to the translocation of glucose transporter type 4 (GLUT4) to
the cell membrane to promote glucose uptake. Downstream targets of these kinases resulting in GLUT4 translocation are unclear. In addition, this pathway also stimulates glycogen synthesis and lipogenesis (Fukushima et al., 2010; Guo, 2014).
In the second pathway, activation (phosphorylation) of the insulin receptor leads to the phosphorylation of the proto-oncogene Casitas b-lineage lymphoma (c-Cbl), which
is in a complex with the adaptor protein CAP. This Cbl-CAP complex then translocates to lipid rafts at the cell membrane, and recruits a complex of Crk, an adaptor protein, and C3G, a guanine nucleotide exchange protein, into lipid rafts. C3G activates the GTP-binding protein family, TC10, which leads to the translocation of GLUT4 to the cell membrane. The downstream signaling steps of this TC10-mediated translocation of GLUT4 are not yet clear (Bryant et al., 2002).
1.2.3 Glucose uptake
The transport of glucose across the cell membrane is important in reducing blood glucose levels, maintaining glucose homeostasis and yielding cells with sufficient energy. Glucose is a polar molecule which cannot pass the bilayer lipid cell membrane on its own. Glucose transport is facilitated by glucose transporters (GLUTs), which
allow transport of glucose down its concentration gradient. To date, a total of 14 GLUTs (i.e. GLUT 1-14) have been identified (Carvalho et al., 2011; Pyla et al., 2013), each with different kinetics and efficiency of glucose (and hexose) transport, and with tissue-specific distribution, which is species dependent (Thorens and Mueckler, 2010).
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Also, the expression of these transporters is differentially regulated. Therefore, each transporter plays a different role in the promotion of glucose uptake in various tissues and the regulation glucose homeostasis.
GLUT1-4 are the most well established GLUTs (Thorens and Mueckler, 2010). GLUT1 is widely distributed over various tissues, in humans most abundant in erythrocytes and in the brain. It stimulates glucose uptake independently of insulin, and is also capable of transporting other hexoses such as mannose and galactose. GLUT2 is the major transporter in hepatocytes. It is also expressed in the intestinal mucosa, renal tubules, pancreatic β-cells and in the brain. Its affinity for glucose is low compared to GLUT1. It is also capable of transporting mannose, galactose and fructose. GLUT3 is most abundant in the brain, but is also expressed in other tissues, in a species-specific matter. It has a high affinity for glucose compared to GLUT1, it operates independent of insulin, and is also capable of transporting galactose, mannose, maltose and xylose. GLUT4 is the main transporter in insulin-sensitive cells and tissues, such as skeletal muscles cells, heart cells and adipose tissue, and is therefore extremely important in the regulation of post-prandial glucose homeostasis (see previous section). Its affinity to glucose is similar to that of GLUT1. For further reading on the characteristics and tissue-specific distribution of the different GLUTs, please see (Zhao and Keating, 2007; Thorens and Mueckler, 2010).
1.2.4 Insulin resistance
Insulin is important for the promotion of glucose uptake/utilization in skeletal muscle, adipose tissue and various vital organs. Insulin resistance is defined as a decreased ability of insulin to promote glucose uptake in insulin-sensitive cells and tissues. Pronounced (i.e., chronic) hyperglycemia, hyperinsulinemia and glucosuria are therefore indications of insulin resistance (Petersen and Shulman, 2006; Ye, 2013). Insulin resistance is often distinguished into hepatic insulin resistance and peripheral insulin resistance. Hepatic insulin resistance refers to the failure of insulin to adequately suppress hepatic endogenous glucose production, whereas peripheral insulin resistance refers to failure of insulin to adequately promote glucose uptake in peripheral tissues (e.g. skeletal muscle and adipose tissues). The causes of insulin resistance are not clear, but insulin resistance is considered a multi-factorial disorder, resulting from nutritional, physiological, genetic and environmental factors (Pedersen, 1999; Li et al., 2013; Roberts et al., 2013).
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Figure 1.2 | Insulin signaling pathways for glucose transport (Grusovin and Macaulay, 2003). At least two
pathways have been implicated in insulin-stimulated glucose transport. In the first pathway, insulin binds to the insulin receptor, resulting in tyrosine phosphorylation and docking of insulin receptor substrates (IRSs). IRS provides binding site for several proteins including phosphoinositide 3-kinase (PI3K), which activates Protein kinase B (PKB) and Protein kinase C (PKC), resulting in the translocation of glucose transporter type 4 (GLUT4) to the cell membrane to promote glucose uptake. In the second pathway, insulin binds to the insulin receptor, resulting in tyrosine phosphorylation and docking of Casitas b-lineage lymphoma (Cbl) which is in a complex with the adaptor protein CAP. This Cbl-CAP complex translocates to lipid rafs, which recruits the adaptor protein Crk and C3G, a guanine nucleotide exchange protein. G3G activates the GTP-binding protein family TC10, which leads to translocation of GLUT4 to promote glucose uptake. The downstream signaling steps of this TC10-mediated GLUT4 translocation are not yet clear.
Insulin sensitivity can be influenced by diet composition. Chronic excessive energy consumption promotes insulin resistance through various processes, which include the stimulation of insulin secretion (which can lead to hyperinsulinemia), triglycerides synthesis and fat accumulation (Bessesen, 2001; Wilcox, 2005).
Over the last two decades, effects of several macronutrients on insulin resistance have been studied extensively. Especially high dietary saturated fat intake has been associated with insulin resistance (Wilcox, 2005; Weickert, 2012). Not all types of dietary fat negatively affect insulin sensitivity (Gadgil et al., 2013). Omega-3 fatty acids, for example, have shown to have a positive effect on insulin sensitivity, whereas saturated and trans fatty acids have been associated with insulin resistance (Rivellese et al., 2002). The fatty acid composition might play an important role on the long-term development of insulin resistance, via effects on cell membrane composition (as fatty acids are important components of cell membranes), cell signaling and membrane fluidity.
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For carbohydrates, the glycemic index (GI) has been introduced in an attempt
to rank carbohydrates-containing diets based their glycemic response in blood. Postprandial hyperglycemia and hyperinsulinemia have been associated with the development of insulin resistance. Therefore, diets with a low GI may reduce the risk of development of insulin resistance. Evidence on fructose (low GI), however, has shown that this is not always the case, as high-fructose diets have also been associated with insulin resistance (Bessesen, 2001; Balakumar et al., 2016). Many low GI diets are rich in fiber. Dietary fibers indirectly affect insulin secretion and action through effects on transit time, gut mobility, gastrointestinal hormone secretion (e.g. GIP and GLP-1) and colonic fermentation products (Wilcox, 2005). Starches that are not digested in the small intestine but fermented in the colon (called resistant starch) have been associated with decreased postprandial glucose (low GI) and insulin response, and improved whole-body insulin sensitivity (Robertson et al., 2005; Johnston et al., 2010). Pre-biotic fibers such as short-chain fructo-oligosaccharides (scFOS) selectively modulate the
composition of the gut microbiota (Respondek et al., 2013). The supplementation of these fibers has been associated with improved glucose metabolism and insulin sensitivity in various animal species (Kaufhold et al., 2000; Respondek et al., 2008; Respondek et al., 2011; Respondek et al., 2013). The exact mechanisms are however not known and are currently under investigation.
Some dietary amino acids have an insulinotrophic effect and thus stimulate insulin secretion, which in turn stimulates glucose uptake. However, some proteins also stimulate glucagon secretion and gluconeogenesis. There is increasing evidence suggesting that long-term high-protein intake may lead to insulin resistance and type-2 diabetes (Sluijs et al., type-2010; Mei et al., type-2014). Especially branched-chain amino acids (i.e., leucine, iso-leucine and valine) have been associated with insulin resistance in human and rodents (Lynch and Adams, 2014; Yoon, 2016). The role of branched-chain amino acids in the development of insulin resistance is unclear, as it is not known whether branched-chain amino acids are causative agents or biomarkers of insulin resistance (and type-2 diabetes). It is been hypothesized that branched-chain amino acids mediate activation the mammalian target of rapamycin complex (mTOR) 1,
which affects insulin signaling at early stage (Lynch and Adams, 2014). Further research is needed.
1.2.5 Physiological causes of insulin resistance
There are several physiological factors that may be involved in the development of insulin resistance. First, a decrease in the number of insulin receptors in skeletal muscle cells, and adipose tissue may cause insulin resistance (Accili et al., 1989; Taylor et al., 1990). In humans and rodents the number of insulin receptors decreases with age (Pagano et al., 1981; Torlińska et al., 2000). Nutritional factors may also affect the number of insulin receptors. For example, a decreased number of insulin receptors in skeletal muscle cells was found in insulin resistant calves fed a high lactose MR diet,
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compared to a standard lactose MR diet (Hugi et al., 1998). Hyperinsulinemia affects the affinity (but not the number) of insulin receptor in skeletal muscle cells (Soman and DeFronzo, 1980). Second, a reduction in the number of GLUT4 or impaired translocation of GLUT4 in skeletal muscle cells and adipose tissue may also cause insulin resistance. In a study with rats, translocation of GLUT4 in skeletal muscle cells and adipose tissue was reduced in insulin resistant rats (Cartee et al., 1993). A mice study showed that disruption of GLUT4 selectivity in skeletal muscle cells induces glucose intolerance and insulin resistance (Zisman et al., 2000). Third, defects in post-receptor insulin signaling can cause insulin resistance (Miura et al., 2001). For example, when insulin binds to its receptor it activates IRSs, which in turn activates the PI3K/AKT pathway ultimately resulting in translocation of GLUT4 to promote glucose uptake. PI3K/AKT, however, also activates the mTOR/S6 kinase pathway, which in turn causes phosphorylation and degradation of IRSs, and impairs insulin signaling. Therefore, hyperactivation of mTOR can cause insulin resistance (Blagosklonny, 2013). The mTOR/S6 kinase pathway is also activated by macronutrients. In humans and rodents, glucose, fatty acids and amino acids have all shown to activate mTOR, possibly resulting in insulin resistance. Therefore, low caloric diets may reduce the risk of developing insulin resistance. Several other physiological factors are also implicated in insulin resistance, which include inflammation and oxidative stress (Hotamisligil, 2006; Shoelson et al., 2006; Park et al., 2009).
1.3 Methods for assessing insulin sensitivity and glucose homeostasis
Several methods are available to assess insulin sensitivity/resistance and glucose homeostasis in humans and animals. These methods can be divided into direct and indirect methods. Each method has its own advantages and disadvantages. Factors such as invasiveness, reproducibility and validity as well as costs, the necessary expertise and qualified personnel play an important role when choosing a suitable method. In this section we will discuss the most frequently used methods.
1.3.1 Euglycemic hyperinsulinemic clamp
The euglycemic-hyperinsulinemic clamp technique, originally developed by DeFronzo et al. (1979), is considered as the “golden standard” for the assessment of insulin sensitivity (Muniyappa et al., 2008). This method is usually implemented after an overnight fasting period of at least 12 h, to achieve constant glucose turnover, and to prevent disturbances from pancreatic insulin production and exogenous glucose derived from the diet. Two catheters are placed, one for glucose and one for insulin infusion. First, insulin is infused to increase blood insulin to hyperinsulinemic levels (typically ~ 100 mU/L), which inhibits pancreatic insulin production. The infusion is kept constant for 2 to 4 h. The infusion of insulin causes a drop in blood glucose
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levels. To maintain euglycemic levels glucose is infused. By measuring the glucose level regularly (5-15 min), the glucose infusion rate can be adjusted to reach a steady-state. The glucose infusion rate at steady-state (corrected for body weight) divided by the blood insulin level at steady is a measure of whole-body insulin sensitivity. The higher the amount of glucose infusion needed to compensate for glucose clearance from the blood, the more sensitive the body tissues are to insulin. The main advantage of the euglycemic-hyperinsulinemic clamp technique is that it directly measures whole-body glucose disposal at a given level of insulinemia under steady-state conditions. Also, the approach is conceptually straightforward. The main limitations of this technique are that it is time-consuming, expensive and labor-intensive and requires experienced personnel. Hence, for large studies (e.g. epidemiological and large clinical or non-clinical studies) this technique is not feasible (Buchanan et al., 2010).
1.3.2 Frequently sampled intravenous glucose tolerance test
The frequently sampled intravenous glucose tolerance test, developed by Bergman et al. (1979), is considered the “silver standard” for the assessment of insulin sensitivity. The test is an indirect method for the assessment of insulin sensitivity, and uses blood glucose and insulin levels obtained during a frequently sampled intravenous glucose tolerance test to calculate insulin sensitivity. After overnight fasting, an intravenous bolus of glucose (~ 0.3 g/kg body weight) is infused at t= 0 min. At t= 20 min an intravenous bolus of insulin (~ 0.03 IU/kg body weight) is infused. Blood samples are taken between t= -10 and 180 min, and glucose and insulin concentrations are determined. These data are subsequently subjected to minimal model analysis, which is a mathematical model that integrates glucose–insulin kinetics and their relationship. The model is embedded in the computer program MINMOD, and generates an index of insulin sensitivity (Pacini and Bergman, 1986; Boston et al., 2003). The main advantage of the frequently sampled intravenous glucose tolerance test is that, in addition to the insulin sensitivity index, several other parameters can be estimated, which include glucose effectiveness and β–cell function (insulin secretion). Compared to the euglycemic-hyperinsulinemic clamp it is slightly less labor-intensive, less expensive, does not require steady-state conditions, or experienced personnel to maintain constant insulin infusion. The main limitations of this technique are that it is labor-intensive, as it requires multiple blood sampling over a 3-h period, and also a special software package is needed. The model itself oversimplifies the physiology of glucose homeostasis (Muniyappa et al., 2008). 1.3.3 Insulin suppression test
The insulin suppression test is another direct method for the assessment of insulin sensitivity, and was originally developed by Shen et al. (1970). After overnight fasting, somatostatin (250 g/h) is intravenously infused to suppress endogenous insulin production. Simultaneously, glucose and insulin are infused at ~ 0.06 g/kg body weight/min and 0.05 IU/min, respectively, for 180 min at constant rate. Blood samples
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are taken every 30 min for the first 150 min for the determination of glucose and insulin. Between 150-180 min steady-state is assumed. During steady-state blood is collected every 10 min. The steady-state plasma insulin level is generally similar (but not always) among subjects. Therefore, the steady-state plasma glucose level gives an estimation of insulin sensitivity. A higher steady-state plasma glucose level indicates lower tissue (but not whole-body) insulin sensitivity. The main advantage of the insulin suppression test is that it directly measures tissue insulin sensitivity (no mathematical models needed) and is highly reproducible (Workeneh et al., 2010). Also, it is less labor-intensive and less technically demanding than the euglycemic-hyperinsulinemic clamp (as it does not require variable infusions). The main limitations of this technique are that it is expensive, requires experienced personnel (although less technically demanding than the euglycemic-hyperinsulinemic clamp), and there is a risk of hypoglycemia in insulin-sensitive subjects (Muniyappa et al., 2008). Also, similar to the euglycemic-hyperinsulinemic clamp this technique is not feasible for large studies.
1.3.4 Oral glucose/meal tolerance test
The oral glucose/meal tolerance test is commonly used to assess glucose homeostasis. In a clinical setting it is frequently used to diagnose glucose intolerance and type 2 diabetes (Tuomilehto, 2002). After overnight fasting, a standard glucose load or standard meal is given, and blood samples are collected at baseline and in regular intervals after consumption for the determination of blood glucose and insulin. These determinations are used to assess glucose homeostasis. Glucose and insulin dynamics in the oral/meal tolerance test mimic physiological conditions more closely than the euglycemic-hyperinsulinemic clamp, insulin suppression or the frequently sampled intravenous glucose tolerance test. Also, the oral/meal tolerance test is cost-effective and simple to execute. Although the test provides information useful for assessing glucose homeostasis/tolerance, it does not assess insulin sensitivity per se. Also, reproducibility can be a problem as gastric emptying, glucose absorption, splanchnic glucose uptake and incretin effects can vary significantly, even within the same individual. In cases when only insulin sensitivity is of interest, simple surrogate indexes of insulin sensitivity/resistance can be a good option.
1.3.5 Simple surrogate indexes of insulin sensitivity/resistance
The simple surrogate indexes of insulin sensitivity/resistance can be divided in two categories; 1) indexes derived from steady-state conditions, and 2) indexes derived from dynamic tests.
Indexes derived from steady-state conditions are determined from a single blood sample collected after overnight fasting. During fasting, blood glucose is homeostatically maintained within normal ranges by an equilibration between hepatic glucose production, pancreatic insulin secretion, and whole-body glucose uptake. Therefore, the relationship between fasting blood glucose and insulin levels reflects insulin
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sensitivity/resistance. Steady-state indexes of insulin sensitivity/resistance include the homeostasis model assessment (Matthews et al., 1985) and the quantitative insulin sensitivity check index (Katz et al., 2000). Both methods are similar, as they are based on the same physiological principle, with the quantitative insulin sensitivity check index being the inverse logarithm of the homeostasis model assessment. Both methods have proven to be practical and useful in epidemiological studies. Furthermore, both methods have shown to correlate well with the euglycemic-hyperinsulinemic clamp in several studies (Katz et al., 2000; Chen et al., 2003; Tam et al., 2012). These indexes are simple, inexpensive, minimally invasive and can be applied in almost every setting.
Other surrogate indexes use information derived from dynamic tests such as the oral glucose or meal tolerance test. These indexes include the Stumvoll index (Stumvoll et al., 2000), Matsuda index (Matsuda and DeFronzo, 1999), Gutt index (Gutt et al., 2000), Avignon index (Avignon et al., 1999) and the Belfiore index (Belfiore et al., 1998), and have been validated against the euglycemic-hyperinsulinemic clamp in humans in several studies (Matsuda and DeFronzo, 1999; Gutt et al., 2000; Mari et al., 2001). For animals, however, the validity of these indexes has not been assessed extensively. Regardless, poor reproducibility of the oral glucose/meal tolerance test due to variability gastric emptying, glucose absorption, splanchnic glucose uptake and incretin effects limits the use of these indexes in practice. Also, fasting indexes are usually preferred because they are more cost-effective and less labor-intensive than dynamic tests.
1.4 Metabolomics
Metabolomics (metabonomics or metabolic profiling) is the analysis of all small-molecule metabolites (typically ≤ 1000 m/z) in body fluids, tissues and cells. In contrast to genes and proteins, the metabolome reflects true biological endpoints of a condition (Figure 1.3). Using highly advanced analytical techniques, such as Mass spectrometry and Nuclear magnetic resonance spectroscopy coupled to (ultra)-High-performance liquid chromatography, Gas chromatography or Capillary electrophoresis, metabolomics focuses on identifying and monitoring alterations in metabolite profiles as a result of pathology, nutritional interventions, drug interventions, genetic or environmental factors. Its potential has been demonstrated in various studies (Jansson et al., 2009; Ametaj et al., 2010; Jung et al., 2013). In humans, several markers of insulin resistance have been discovered using this approach (Gall et al., 2010). These include, for example, α-hydroxybutyrate, linoleoyl-glycerophosphocholine, glycine, creatine and branched chain amino acids. Based on 26 of these marker metabolites a model (Quantose algorithm) was developed that can predict insulin resistance (Cobb et al., 2013).
The main advantage of metabolomics is that one can obtain information of hundreds of metabolites from a very limited volume sample. This information can be used to discover and highlight metabolites and pathways that are related to a
patho-1
physiological development for example. Metabolomics can be used in many fields, such as biomarker discovery, diagnostic research, nutritional research, toxicology and drug discovery. However, it is relatively expensive and requires special apparatus and experienced personnel. In addition, the metabolome is complex both in terms of chemical diversity (i.e., polarity, solubility, molecular weight, volatility, etc.) and dynamic range (varying in several orders of magnitude). Also, the number of metabolites present in different organisms is still unknown. For humans, for example, more than 8,000 metabolites have been identified so far according to the human metabolome database (Wishart et al., 2013). In the plant kingdom, over 200,000 metabolites are estimated (Weckwerth, 2003; Saito and Matsuda, 2010). Due to the great dynamic range and chemical diversity it is currently infeasible to analyze all metabolites using one analytical platform. Therefore, in practice, multiple analytical platforms are used when feasible. This increases the power of metabolomics. However, even so, analyzing the complete metabolome remains a great challenge.
Figure 1.3 | Schematic representation of the “omics” cascade. The metabolome reflects true biological
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1.5 Veal calf production and nutrition
Meat from young calves (< 8 months of age) is called veal. The most common type of veal originates from calves which are typically up to six months of age, and have been fed mainly on milk replacer which is typically low in iron. The meat is therefore sometimes called ‘white veal’. Each year, around six million calves are raised for veal in the EU-28 countries (Source: Eurostat), with the majority raised for ‘white veal’ production.
In practice, male dairy and female calves are purchased from dairy farms at approximately 14 days of age and are transported to specialized gathering facilities, were they are mixed and re-grouped. Then, calves are transported to specialized veal farms where they are housed individually during the first 4-6 weeks to allow individual monitoring. Thereafter, calves are group-housed until slaughter age (approximately seven months in the Netherlands).
Veal calves, in contrast to ruminating (dairy) calves, are fed MR until slaughter, to allow a high average daily weight gain as well as a typical meat quality (characterized by its paleness and tenderness). The MR contains highly digestible nutrients such as ~50% lactose, ~20% crude fat and ~20% crude protein. In this composition, energy is mainly provided by lactose and crude fat (~45% and 35% of metabolizable energy intake). Although it is nowadays mandatory to feed veal calves with solid feed (i.e., concentrates and roughages), carbohydrates and fat from MR still contribute approximately 75% to the digestible energy intake (Labussiere et al., 2009a). Upon ingestion of the MR, the esophageal groove closes, allowing the MR to by-pass the reticulorumen (Abe et al., 1979). As a result, the MR flows directly into the abomasum, and from there into the small intestine (Figure 1.4). Once in the small intestine, nutrients are enzymatically hydrolyzed and absorbed, like in monogastric animals. In contrast, calves raised for beef production or breeding, between 4-6 months of age are predominantly fed concentrates and roughages (Blum and Harmon, 1999). These solids enter the reticulorumen, where they are fermented by the microbial population. Therefore, these calves operate as true ruminants.
Figure 1.4 | Schematic representation of the milk flow in milk-fed calves. In milk-fed calves, the esophageal
groove closes, allowing the milk to by-pass the recticulorumen and flow directly into the abomasum. Source: Merrick Animal Nutrition, Inc.
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1.6 Glucose homeostasis and insulin resistance in veal calves
Veal calves often show disturbances in glucose homeostasis and insulin sensitivity. These problems have been identified in heavy veal calves (4-6 months of age; in the second phase of the fattening period), and are characterized by high incidences of hyperglycemia, hyperinsulinemia and urinary glucose excretion (Wijayasinghe et al., 1984; Palmquist et al., 1992; Hostettler-Allen et al., 1994; Hugi et al., 1997; Hugi et al., 1998). Also, hepatic steatosis (excessive accumulation of fat in the liver) has also been reported (Gerrits et al., 2008). In heavy veal calves, blood glucose and insulin increase excessively after feeding and remain high, not returning to basal levels even after 4-5 h (Hugi et al., 1997; Vicari et al., 2008). In healthy humans, blood glucose and insulin typically return to basal levels within 2-3 h after feeding (Eelderink et al., 2012b). The symptoms observed in veal calves resemble those commonly observed in humans suffering from type 2 diabetes.
The causes behind disturbed glucose homeostasis and insulin sensitivity in veal calves are not clear. The etiology of insulin resistance in veal calves may be complex and multifactorial (Hostettler-Allen et al., 1994; Gerrits et al., 2008). The problems with glucose homeostasis and insulin sensitivity in veal calves appear to be age-dependent (Hugi et al., 1997). These problems have been identified especially in calves at end of their fattening period. This could perhaps be due to the high lactose content in veal calf diets. One study in veal calves has suggested that insulin sensitivity is modulated by supplemental lactose, in an age-dependent manner (Hugi et al., 1998). Decreasing the amount of lactose in veal calf diets will lead to reduced postprandial glucose and insulin responses, which could potentially reduce (or prevent) problems with glucose homeostasis and insulin sensitivity in veal calves. Apart from lactose, also the high fat content in veal calf diets might be causing problems with glucose homeostasis and insulin sensitivity. In humans, a high fat intake was found to be independently correlated with insulin resistance. Weaned non-ruminant animal species (e.g. pigs) do not suffer from insulin resistance, despite their much higher glucose (but lower fat) intake than veal calves and humans, which indicates that interactions between dietary fatty acids and glucose may be crucial for disturbing glucose metabolism in veal calves. Another factor that could affect glucose homeostasis and insulin sensitivity in veal calves is the discrepancy between the diet of veal calves and their ontogenetic background. In nature, calves between 4 and 6 months of age are grazing and plant fragments are fermented in the rumen along with the production of short-chain fatty acids as a major energy source. Therefore, veal calves, which are ontogenetic ruminants, may not be equipped with the genetic capacity to keep dealing with large amounts of dietary lactose adequately, when they become older.
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1.7 Scope of this thesis
The development of insulin resistance is not only a problem in human, but also in various animal species and might lead to type 2 diabetes.
In this project, we focused on the understanding of changes in glucose homeostasis and the development of insulin resistance in veal calves, and on quantifying the influence of several dietary modulations.
We studied in particular:
1. The age-related development of insulin resistance
2. The effects of partial replacement of the lactose in calf MR by other energy sources, such as glucose, fructose and glycerol, on glucose homeostasis and insulin resistance
3. The effects of dietary scFOS on glucose homeostasis and insulin resistance 4. The applicability of metabolic profiling techniques to find metabolites
(biomarkers) and pathways related to insulin resistance The following research questions were addressed:
1. Are insulin sensitivity and glucose homeostasis in veal calves affected by the replacement of a substantial amount of the lactose in a calf milk replacer by fat?
Lactose and fat are the main energy sources in veal calf nutrition. However, the possible contribution of these dietary energy sources to a deteriorated glucose homeostasis and insulin resistance is currently unknown. Standardized studies in which lactose and fat are exchanged (iso-energetically) may reveal the contribution of these dietary energy sources to the development of insulin resistance in calves. Therefore, an experiment was designed to compare effects of a high-lactose vs. a high-fat MR on glucose homeostasis and insulin sensitivity in heavy veal calves (Chapter 2).
2. Can plasma metabolomic profiling techniques be used to study and identify insulin resistance in veal calves?
Approximately 50% of heavy veal calves on a high-lactose or high-fat MR diet develop insulin resistance. Therefore, it is worthwhile to study the patho-physiological mechanisms behind insulin resistance, and detect biomarkers that perhaps can be used to detect insulin resistance (or a decrease in insulin sensitivity) at an early stage. By detecting insulin resistance at an early stage, management and feeding strategies could perhaps be adopted to prevent this problem. Therefore, using metabolic profiling techniques, we attempted to discover pathways and markers that are associated with insulin resistance in veal calves (Chapter 3).
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3. Are insulin sensitivity and glucose homeostasis in veal calves affected by the replacement of a substantial amount of the lactose in a calf milk replacer by glucose, fructose or glycerol?
Glucose, fructose and glycerol are possible alternatives to lactose. Especially fructose and glycerol are expected to positively affect glucose homeostasis, and possibly improve insulin sensitivity. Therefore, an experiment was designed to study the effects of replacing a substantial amount of the dietary lactose in the MR by glucose, fructose or glycerol on glucose homeostasis and insulin sensitivity in veal calves (Chapter 4).
4. Is the age-dependent development of insulin resistance influenced by a strong contrast in feeding strategy (i.e., weaning compared to milk replacer feeding)?
Insulin sensitivity decreases in calves during the first months of life. Yet, it remains unclear whether the decrease in insulin sensitivity can be influenced by strong contrast in feeding strategy (i.e., prolonged MR feeding vs. progressive weaning), or is explained by the ontogenetic development of calves. Therefore, an experiment was designed to assess age-related and diet-induced (i.e., MR only
vs. progressive weaning) changes in glucose homeostasis and insulin sensitivity
in calves during the first three months of life (Chapter 5).
5. Can supplementation of scFOS improve insulin sensitivity and glucose homeostasis in young veal calves?
Studies in various animal species have shown that dietary scFOS improved whole body insulin sensitivity. In dogs and horses with obesity, for example, an increase in insulin sensitivity was measured after feeding FOS for a period of 6 weeks. Whether FOS supplementation can prevent the decrease/or improve insulin sensitivity in veal calves is not known. Therefore, an experiment was designed to assess the effect of FOS supplementation on insulin sensitivity and glucose homeostasis in veal calves during their first three months of life
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1.8 References
Abe, M., T. Iriki, K. Kondoh, and H. Shibui. 1979. Effects of nipple or bucket feeding of milk-substitute on rumen by-pass and on rate of passage in calves Br. J. Nutr. 41:175-181.
Accili, D., C. Frapier, L. Mosthaf, C. McKeon, S. C. Elbein, M. A. Permutt, E. Ramos, E. Lander, A. Ullrich, and S. I. Taylor. 1989. A mutation in the insulin receptor gene that impairs transport of the receptor to the plasma membrane and causes insulin-resistant diabetes. EMBO J. 8:2509-2517.
Ametaj, B. N., Q. Zebeli, F. Saleem, N. Psychogios, M. J. Lewis, S. M. Dunn, J. Xia, and D. S. Wishart. 2010. Metabolomics reveals unhealthy alterations in rumen metabolism with increased proportion of cereal grain in the diet of dairy cows. Metabolomics 6:583-594.
Avignon, A., C. Boegner, D. Mariano-Goulart, C. Colette, and L. Monnier. 1999. Assessment of insulin sensitivity from plasma insulin and glucose in the fasting or post oral glucose-load state. Int. J. Obes. Relat. Metab. Disord. 23.
Balakumar, M., L. Raji, D. Prabhu, C. Sathishkumar, P. Prabu, V. Mohan, and M. Balasubramanyam. 2016. High-fructose diet is as detrimental as high-fat diet in the induction of insulin resistance and diabetes mediated by hepatic/pancreatic endoplasmic reticulum (ER) stress. Mol. Cell. Biochem. 423:93-104. Barros, R. P. A., C. Gabbi, A. Morani, M. Warner, and J. A. ke Gustafsson. 2009. Participation of ERα and ERβ
in glucose homeostasis in skeletal muscle and white adipose tissue. Am. J. Physiol. Endocrinol. Metab. 297:E124–E133.
Belfiore, F., S. Iannello, and G. Volpicelli. 1998. Insulin sensitivity indices calculated from basal and OGTT-induced insulin, glucose, and FFA levels. Mol. Genet. Metab. 63:134-141.
Bergman, R. N., Y. Z. Ider, C. R. Bowden, and C. Cobelli. 1979. Quantitative estimation of insulin sensitivity. Am. J. Physiol. 236:E667-E677.
Bessesen, D. H. 2001. The role of carbohydrates in insulin resistance. The Journal of nutrition 131:2782S-2786S. Blagosklonny, M. V. 2013. TOR-centric view on insulin resistance and diabetic complications: perspective for
endocrinologists and gerontologists. Cell Death Dis. 12:e964.
Blum, J. W. and D. L. Harmon. 1999. Endocrine and metabolic aspects in milk-fed calves. Domest. Anim. Endocrinol. 17:219-230.
Boston, R. C., D. Stefanovski, P. J. Moate, A. E. Sumner, R. M. Watanabe, and R. N. Bergman. 2003. MINMOD Millennium: a computer program to calculate glucose effectiveness and insulin sensitivity from the frequently sampled intravenous glucose tolerance test. Diabetes Technol. Ther. 5:1003-1015.
Bryant, N. J., R. Govers, and D. E. James. 2002. Regulated transport of the glucose transporter GLUT4. Nature 3:267-277.
Buchanan, T. A., R. M. Watanabe, and A. H. Xiang. 2010. Limitations in Surrogate Measures of Insulin Resistance. J. Clin. Endocrinol. Metab. 95:4874-4876.
Cartee, G. D., C. Briggs-Tung, and E. W. Kietzke. 1993. Persistent effects of exercise on skeletal muscle glucose transport across the life-span of rats. J. Appl. Physiol. 75:972-978.
Carvalho, K. C., I. Isabela, W. Cunha, R. M. Rocha, F. R. Ayala, M. M. Cajaíba, M. D. Begnami, R. S. Vilela, G. R. Paiva, R. G. Andrade, and F. A. Soares. 2011. GLUT1 expression in malignant tumors and its use as an immunodiagnostic marker. Clinics 66:965-972.
Chen, H., G. Sullivan, L. Q. Yue, A. Katz, and M. J. Quon. 2003. QUICKI is a useful index of insulin sensitivity in subjects with hypertension. Am. J. Physiol. Endocrinol. Metab. 284:E804-E812.
Cobb, J., W. E. Gall, K. Adam, P. Nakhle, E. Button, J. Hathorn, K. Lawton, M. Milburn, R. Perichon, M. Mitchell, A. Natali, and E. Ferrannini. 2013. A novel fasting blood test for insulin resistance and prediabetes. J. Diabetes Sci. Technol. 7:100-110.
DeFronzo, R. A., J. D. Tobin, and R. Andres. 1979. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am. J. Physiol. 237:E214-E223.
Denroche, H. C., F. K. Huynh, and T. J. Kieffer. 2012. The role of leptin in glucose homeostasis. J Diabetes Investig. 3:115-129.
Eelderink, C., T. C. W. Moerdijk-Poortvliet, H. Wang, M. Schepers, T. Preston, T. Boer, R. J. Vonk, H. Schierbeek, and M. G. Priebe. 2012a. The glycemic response does not reflect the In vivo starch digestibility of fiber-rich wheat products in healthy men. J. Nutr. 142:258-263.
Eelderink, C., M. Schepers, T. Preston, R. J. Vonk, L. Oudhuis, and M. G. Priebe. 2012b. Slowly and rapidly digestible starchy foods can elicit a similar glycemic response because of differential tissue glucose uptake in healthy men. Am. J. Clin. Nutr. 96:1017-1024.
1
Fukushima, A., K. Loh, S. Galic, B. Fam, B. Shields, F. Wiede, M. L. Tremblay, M. J. Watt, S. Andrikopoulos, and T. Tiganis. 2010. T-cell protein tyrosine phosphatase attenuates STAT3 and insulin signaling in the liver to regulate gluconeogenesis. Diabetes 59:1906-1914.
Gadgil, M. D., L. J. Appel, E. Yeung, C. A. M. Anderson, F. M. Sacks, and E. R. Miller. 2013. The effects of carbohydrate, unsaturated fat, and protein intake on measures of insulin sensitivity. Diabetes Care. 36:1132-1137.
Gall, W. E., K. Beebe, K. A. Lawton, K. P. Adam, M. W. Mitchell, P. J. Nakhle, J. A. Ryals, M. V. Milburn, M. Nannipieri, S. Camastra, A. Natali, and E. Ferrannini. 2010. alpha-Hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS One 5:e10883. Gerrits, W. J. J., J. J. G. C. van den Borne, and J. W. Blum. 2008. Low-dietary protein intake induces problems
with glucose homeostasis and results in hepatic steatosis in heavy milk-fed calves. Domest. Anim. Endocrinol. 35:121-129.
Gilbert, M. S. 2015. Replacing lactose from calf milk replacers : effects on digestion and post-absorptive metabolism. Vol. PhD Thesis. Wageningen University, Wageningen, The Netherlands.
Grusovin, J. and S. L. Macaulay. 2003. Snares for GLUT4-mechanisms directing vesicular trafficking of GLUT4. Front. Biosci. 8:d620-d641.
Guo, S. 2014. Insulin signaling, resistance, and the metabolic syndrome: insights from mouse models into disease mechanisms. The Journal of endocrinology 220:T1-T23.
Gutt, M., D. C.L., S. B. Spitzer, M. M. Llabre, M. Kumar, E. M. Czarnecki, N. Schneiderman, J. S. Skyler, and J. B. Marks. 2000. Validation of the insulin sensitivity index [ISI(0,120)]: comparison with other measures. Diabetes Res. Clin. Pract. 47:177-184.
Hayes, M. R., E. G. Mietlicki-Baase, S. E. Kanoski, and B. C. De Jonghe. 2014. Incretins and Amylin: Neuroendocrine Communication between the Gut, Pancreas, and Brain in Control of Food Intake and Blood Glucose. Annu. Rev. Nutr. 34:237-260.
Hostettler-Allen, R. L., L. Tappy, and J. W. Blum. 1994. Insulin resistance, hyperglycemia, and glucosuria in intensively milk-fed calves. J. Anim. Sci. 72:160-173.
Hotamisligil, G. S. 2006. Inflammation and metabolic disorders. Nature 444:860-867.
Hugi, D., R. M. Bruckmaier, and J. W. Blum. 1997. Insulin resistance, hyperglycemia, glucosuria, and galactosuria in intensively milk-fed calves: dependency on age and effects of high lactose intake. J. Anim. Sci. 75:469-482.
Hugi, D., L. Tappy, H. Sauerwein, R. M. Bruckmaier, and J. W. Blum. 1998. Insulin-dependent glucose utilization in intensively milk-fed veal calves is modulated by supplemental lactose in an age-dependent manner. J. Nutr. 128:1023-1030.
Jansson, J., B. Willing, M. Lucio, A. Fekete, J. Dicksved, J. Halfvarson, C. Tysk, and P. Schmitt-Kopplin. 2009. Metabolomics reveals metabolic biomarkers of crohn’s disease. PLoS One 4:e6386.
Johnston, K. L., E. L. Thomas, J. D. Bell, G. S. Frost, and M. D. Robertson. 2010. Resistant starch improves insulin sensitivity in metabolic syndrome. Diabet. Med. 27:391-397.
Jung, J., S. H. Kim, H. S. Lee, G. S. Choi, Y. S. Jung, D. H. Ryu, H. S. Park, and G. S. Hwang. 2013. Serum metabolomics reveals pathways and biomarkers associated with asthma pathogenesis. Clin. Exp. Allergy 43:425-433.
Kanzaki, M. 2006. Insulin receptor signals regulating GLUT4 translocation and actin dynamics. Endocr. J. 53:267-293.
Katz, A., S. S. Nambi, K. Mather, A. D. Baron, D. A. Follmann, G. Sullivan, and M. J. Quon. 2000. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J. Clin. Endocrinol. Metab. 85:2402-2410.
Kaufhold, J., H. M. Hammon, and J. W. Blum. 2000. Fructo-oligosaccharide supplementation: effects on metabolic, endocrine and hematological traits in veal calves. J. Vet. Med. A Physiol. Pathol. Clin. Med. 47:17-29.
Labussiere, E., S. Dubois, J. van Milgen, G. Bertrand, and J. Noblet. 2009a. Effect of solid feed on energy and protein utilization in milk-fed veal calves. J. Anim. Sci. 87:1106-1119.
Labussiere, E., G. Maxin, S. Dubois, J. van Milgen, G. Bertrand, and J. Noblet. 2009b. Effect of feed intake on heat production and protein and fat deposition in milk-fed veal calves. Animal 3:557-567.
Li, L., X. Li, W. Zhou, and J. L. Messina. 2013. Acute psychological stress results in the rapid development of insulin resistance. J. Endocrinol. 217:175-184.
1
Lynch, C. J. and S. H. Adams. 2014. Branched-chain amino acids in metabolic signalling and insulin resistance. Nat. Rev. Endocrinol. 10:723-736.
Mari, A., G. Pacini, E. Murphy, B. Ludvik, and J. J. Nolan. 2001. A model-based method for assessing insulin sensitivity from the oral glucose tolerance test. Diabetes Care. 24:539–548.
Matsuda, M. and R. A. DeFronzo. 1999. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 22:1462-1470.
Matthews, D. R., J. P. Hosker, A. S. Rudenski, B. A. Naylor, D. F. Treacher, and R. C. Turner. 1985. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 28:412-419.
Mei, S., X. Yang, H. Guo, H. Gu, L. Zha, J. Cai, X. Li, Z. Liu, B. J. Bennett, L. He, and W. Cao. 2014. A small amount of dietary carbohydrate can promote the HFD-induced insulin resistance to a maximal level. PLoS One 9:e100875.
Miura, T., W. Suzuki, E. Ishihara, I. Arai, H. Ishida, Y. Seino, and K. Tanigawa. 2001. Impairment of insulin-stimulated GLUT4 translocation in skeletal muscle and adipose tissue in the Tsumura Suzuki obese diabetic mouse: a new genetic animal model of type 2 diabetes. Eur. J. Endocrinol. 145:785-790. Muniyappa, R., S. Lee, H. Chen, and M. J. Quon. 2008. Current approaches for assessing insulin sensitivity
and resistance in vivo: advantages, limitations, and appropriate usage. Am. J. Physiol. Endocrinol. Metab. 294:E15-E26.
Pacini, G. and R. N. Bergman. 1986. MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test. Comput. Methods Programs Biomed. 23:113-122.
Pagano, G., M. Cassader, A. Diana, E. Pisu, C. Bozzo, F. Ferrero, and G. Lenti. 1981. Insulin resistance in the aged: the role of the peripheral insulin receptors. Metabolism 30:46-49.
Palmquist, D. L., J. Doppenberg, K. L. Roehrig, and D. J. Kinsey. 1992. Glucose and insulin metabolism in ruminating and veal calves fed high and low fat diets. Domest. Anim. Endocrinol. 9:233-241.
Pantophlet, A. J., S. Wopereis, C. Eelderink, R. J. Vonk, J. H. Stroeve, S. Bijlsma, L. van Stee, I. Bobeldijk, and M. G. Priebe. 2016. Metabolic profiling reveals differences in plasma concentrations of arabinose and xylose after consumption of fiber-rich pasta and wheat bread with differential rates of systemic appearance of exogenous glucose in healthy men. J. Nutr. [Epub ahead of print].
Park, K., M. Gross, D. H. Lee, P. Holvoet, J. H. Himes, J. M. Shikany, and D. R. Jacobs. 2009. Oxidative stress and insulin resistance: the coronary artery risk development in young adults study. Diabetes Care. 32:1302-1307.
Pedersen, O. 1999. Genetics of insulin resistance. Exp. Clin. Endocrinol. Diabetes 107:113-118. Petersen, K. F. and G. I. Shulman. 2006. Etiology of insulin resistance. Am. J. Med. 119:S10-S16.
Pyla, R., N. Poulose, J. Y. Jun, and L. Segar. 2013. Expression of conventional and novel glucose transporters, GLUT1, -9, -10, and -12, in vascular smooth muscle cells. Am. J. Physiol., Cell Physiol. 304:C574-C589. Respondek, F., P. Gerard, M. Bossis, L. Boschat, A. Bruneau, S. Rabot, A. Wagner, and J. C. Martin. 2013.
Short-chain fructo-oligosaccharides modulate intestinal microbiota and metabolic parameters of humanized gnotobiotic diet induced obesity mice. PLoS One 12:e71026.
Respondek , F., K. Myers, T. L. Smith, A. Wagner, and R. J. Geor. 2011. Dietary supplementation with short-chain fructo-oligosaccharides improves insulin sensitivity in obese horses. J. Anim. Sci. 89:77-83. Respondek, F., K. S. Swanson, K. R. Belsito, B. M. Vester, A. Wagner, L. Istasse, and M. Diez. 2008.
Short-chain fructooligosaccharides influence insulin sensitivity and gene expression of fat tissue in obese dogs. J. Nutr. 138:1712-1718.
Rivellese, A. A., C. De Natale, and S. Lilli. 2002. Type of dietary fat and insulin resistance. Ann. N. Y. Acad. Sci. 967:329-335.
Roberts, C. K., L. A. Hevener, and R. J. Barnard. 2013. Metabolic syndrome and insulin resistance: underlying causes and modification by exercise training. Compr. Physiol. 3:1-58.
Robertson, M. D., A. S. Bickerton, A. L. Dennis, H. Vidal, and K. N. Frayn. 2005. Insulin-sensitizing effects of dietary resistant starch and effects on skeletal muscle and adipose tissue metabolism. Am. J. Clin. Nutr. 82:559-567.
Röder, P. V., B. Wu, Y. Liu, and W. Han. 2016. Pancreatic regulation of glucose homeostasis. Exp. Mol. Med. 48:e219.
Saito, K. and F. Matsuda. 2010. Metabolomics for functional genomics, systems biology, and biotechnology. Annu. Rev. Plant Biol. 61:462-489.
1
Shen, S. W., G. M. Reaven, and J. W. Farquhar. 1970. Comparison of impedance to insulin-mediated glucose uptake in normal subjects and in subjects with latent diabetes. J. Clin. Invest. 49:2151-2160.
Sherwin, R. S., H. Shamoon, R. Hendler, L. Saccà, N. Eigler, and M. Walesky. 1980. Epinephrine and the regulation of glucose metabolism: effect of diabetes and hormonal interactions. Metabolism 29:1146-1154.
Shoelson, S. E., S. Lee, and A. B. Goldfire. 2006. Inflammation and insulin resistance. J. Clin. Invest. 161:1793-1801.
Sluijs, I., J. W. Beulens, D. L. van der A, A. M. Spijkerman, D. E. Grobbee, and Y. T. van der Schouw. 2010. Dietary intake of total, animal, and vegetable protein and risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-NL study. Diabetes Care. 33:43-48. Soman, V. R. and R. A. DeFronzo. 1980. Direct evidence for downregulation of insulin receptors by physiologic
hyperinsulinemia in man. Diabetes 29:159-163.
Stanfield, C. L. 2012. The endocrine system: Regulation of energy metabolism and growth. Pages 603-630 in Principles of Human Physiology. 5 ed. K. Churchman, S. Scharf, C. C. Jones, J. McElroy, A. Williams, D. Cogan, and N. Tabor, ed. Pearson Education, London, UK.
Stumvoll, M., A. Mitrakou, W. Pimenta, T. Jenssen, H. Yki-Jarvinen, T. Van Haeften, W. Renn, and J. Gerich. 2000. Use of the oral glucose tolerance test to assess insulin release and insulin sensitivity. Diabetes Care. 23:295-301.
Tam, C. S., W. Xie, W. D. Johnson, W. T. Cefalu, L. M. Redman, and E. Ravussin. 2012. Defining Insulin Resistance From Hyperinsulinemic-Euglycemic Clamps. Diabetes Care. 35:1605-1610.
Taylor, S. I., T. Kadowaki, H. Kadowaki, D. Accili, A. Cama, and C. McKeon. 1990. Mutations in insulin-receptor gene in insulin-resistant patients. Diabetes Care. 13:257-279.
Thorens, B. and M. Mueckler. 2010. Glucose transporters in the 21st Century. Am. J. Physiol. Endocrinol. Metab. 298:E141-E145.
Torlińska, T., P. Maćkowiak, L. Nogowski, T. Hryniewiecki, H. Witmanowski, M. Perz, E. M dry, and K. W. Nowak. 2000. Age dependent changes of insulin receptors in rat tissues. J. Physiol. Pharmacol. 51:871-881.
Tuomilehto, J. 2002. Point: a glucose tolerance test is important for clinical practice. Diabetes Care. 25:1880-1882.
Vicari, T., J. J. G. C. van den Borne, W. J. J. Gerrits, Y. Zbinden, and J. W. Blum. 2008. Separation of protein and lactose intake over meals dissociates postprandial glucose and insulin concentrations and reduces postprandial insulin responses in heavy veal calves. Domest. Anim. Endocrinol. 34:182-195.
Watson, R. T., M. Kanzaki, and J. E. Pessin. 2004. Regulated membrane trafficking of the insulin-responsive glucose transporter 4 in adipocytes. Endocr. Rev. 25:177-204.
Weckwerth, W. 2003. Metabolomics in systems biology. Annu. Rev. Plant Biol. 54:669-689. Weickert, M. O. 2012. Nutritional modulation of insulin resistance. Scientifica 2012:424780.
Wijayasinghe, M. S., N. E. Smith, and R. L. Baldwin. 1984. Growth, health, and blood glucose concentrations of calves fed high-glucose or high-fat milk replacers. J. Dairy Sci. 67:2949-2956.
Wilcox, G. 2005. Insulin and Insulin Resistance. Clin. Biochem. Rev. 26:19-39.
Wishart, D. S., T. Jewison, A. C. Guo, M. Wilson, C. Knox, Y. Liu, Y. Djoumbou, R. Mandal, F. Aziat, E. Dong, S. Bouatra, I. Sinelnikov, D. Arndt, J. Xia, P. Liu, F. Yallou, T. Bjorndahl, R. Perez-Pineiro, R. Eisner, F. Allen, V. Neveu, R. Greiner, and A. Scalbert. 2013. HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Res. 41:D801-D807.
Workeneh, B., F. Abbasi, and G. Reaven. 2010. Fasting urine pH is independent of insulin sensitivity. Am. J. Clin. Nutr. 91:586-588.
Ye, J. 2013. Mechanisms of insulin resistance in obesity. Front. Med. 7:14-24.
Yoon, M. S. 2016. The emerging role of branched-chain amino acids in insulin resistance and metabolism. Nutrients 8:E405.
Zhao, F. and A. F. Keating. 2007. Functional properties and genomics of glucose transporters. Curr. Genomics. 8:113-128.
Zisman, A., O. D. Peroni, E. D. Abel, M. D. Michael, F. Mauvais-Jarvis, B. B. Lowell, J. F. Wojtaszewski, M. F. Hirshman, A. Virkamaki, L. J. Goodyear, C. R. Kahn, and B. B. Kahn. 2000. Targeted disruption of the glucose transporter 4 selectively in muscle causes insulin resistance and glucose intolerance. Nat. Med. 6:924-928.
CHAPTER 2
Substantial replacement of lactose with fat in a high-lactose
milk replacer diet increases liver fat accumulation but does
not affect insulin sensitivity in veal calves
A.J. Pantophlet,1 W.J.J. Gerrits,2 R.J. Vonk,3 and J.J.G.C. van den Borne2
Adapted from
Journal of dairy science 2016; 99 (12):10022–10032 1Department of Pediatrics, Center for Liver, Digestive and Metabolic Diseases,
University Medical Centre Groningen, the Netherlands;
2Animal Nutrition Group, Wageningen University, Wageningen, the Netherlands; 3Centre for Medical Biomics, University Medical Center Groningen, Groningen, the Netherlands
"An investment in knowledge pays the best interest" Benjamin Franklin
-2
Abstract
In veal calves, the major portion of digestible energy intake originates from milk replacer (MR), with lactose and fat contributing approximately 45 and 35%, respectively. In veal
calves older than 4 months, prolonged high intakes of MR may lead to problems with glucose homeostasis and insulin sensitivity, ultimately resulting in sustained insulin resistance, hepatic steatosis, and impaired animal performance. The contribution of each of the dietary energy sources (lactose and fat) to deteriorated glucose homeostasis and insulin resistance is currently unknown. Therefore, an experiment was designed to compare the effects of a high-lactose and a high-fat MR on glucose homeostasis and insulin sensitivity in veal calves. Sixteen male Holstein-Friesian calves (120 ± 2.8 kg of BW) were assigned to either a high-lactose (HL) or a high-fat (HF) MR for 13 consecutive
weeks. After at least 7 weeks of adaptation, whole-body insulin sensitivity and insulin secretion were assessed by euglycemic-hyperinsulinemic and hyperglycemic clamps, respectively. Postprandial blood samples were collected to assess glucose, insulin, and triglyceride responses to feeding, and 24-h urine was collected to quantify urinary glucose excretion. At the end of the trial, liver and muscle biopsies were taken to assess triglyceride contents in these tissues. Long-term exposure of calves to HF or HL MR did not affect whole-body insulin sensitivity (averaging 4.2 ± 0.5 × 10−2 [(mg/kg·min)/(μU/
mL)]) and insulin secretion. Responses to feeding were greater for plasma glucose and tended to be greater for plasma insulin in HL calves than in HF calves. Urinary glucose excretion was substantially higher in HL calves (75 ± 13 g/d) than in HF calves (21 ± 6 g/d). Muscle triglyceride content was not affected by treatment and averaged 4.5 ± 0.6 g/kg, but liver triglyceride content was higher in HF calves (16.4 ± 0.9 g/kg) than in HL calves (11.2 ± 0.7 g/kg), indicating increased hepatic fat accumulation. We conclude that increasing the contribution of fat to the digestible energy intake from the MR from 20 to 50%, at the expense of lactose does not affect whole-body insulin sensitivity and insulin secretion in calves. However, a high-lactose MR increases postprandial glucose and insulin responses, whereas a high-fat MR increases fat accumulation in liver but not muscle.
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2.1 Introduction
Veal calves are fed milk replacer (MR) and solid feed (consisting of concentrates and
roughages). Despite the tendency in recent years to increase the amount of solid feed, the vast majority (60-70%) of the digestible energy intake originates from MR. After feeding, MR bypasses the calf’s rumen and directly enters the abomasum because of closure of the esophageal groove. Milk replacer typically contains highly digestible nutrients such as lactose, fat, and proteins, which provide approximately 45%, 35%, and 20% of the digestible energy intake, respectively.
Prolonged intakes of high amounts of MR have been shown to induce problems with glucose homeostasis and insulin sensitivity in heavy (> 4 months of age) veal calves, characterized by high incidences of hyperglycemia and hyperinsulinemia and increased urinary glucose excretion (Hostettler-Allen et al., 1994; Hugi et al., 1997; Pantophlet et al., 2016a). These problems may result in metabolic and pro-inflammatory diseases as seen in humans (Hotamisligil, 2006; Shoelson et al., 2006) and in hepatic steatosis (Gerrits et al., 2008).
Dietary factors contributing to the disturbed glucose homeostasis and insulin sensitivity in heavy calves have been studied (Hugi et al., 1997, 1998; Pantophlet et al., 2016a), and results indicate that high amounts of lactose may be a factor. Ingesting high amounts of lactose in only 2 daily meals and for a prolonged period (i.e., ~6 months of life) deviates from the ontogenetic background of calves. In nature, calves between 4 and 6 months of age are grazing, and feedstuffs from plant origin are fermented in the rumen, along with short-chain fatty acids being produced as a major energy source. Thus, in nature, a gradual shift occurs from glucose and long-chain fatty acids from milk as main energy sources to short-chain fatty acids originating from rumen fermentation during the first months of the calf’s life. In general, problems with glucose metabolism and insulin sensitivity appear to be age dependent in veal calves (Hugi et al., 1997, 1998; Pantophlet et al., 2016b). Heavy veal calves produce very little fatty acids from glucose (Roehrig et al., 1988; van den Borne et al., 2006), and ingestion of large quantities of glucose perturbs their glucose homeostasis for a substantial period after feeding. This circumstance could explain why a high lactose intake negatively affects glucose metabolism and insulin sensitivity in veal calves (Hugi et al., 1997, 1998) and leads to significant amounts of glucose being excreted in urine (Hugi et al., 1997; Pantophlet et al., 2016a).
Alternatively, the high dietary fat content in MR for calves could affect glucose homeostasis and insulin sensitivity. The composition of the digestible energy in veal calves (i.e., high fat and high carbohydrate content) resembles that of the adult Western human diet (Schwarz et al., 2003), and such high dietary fat intake has consistently been associated with the development of insulin resistance (Randle et al., 1963; Storlien et al., 1996; Frayn, 2003; Müller and Kersten, 2003). In rodents, fat-induced problems with insulin sensitivity may operate via several mechanisms, including
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triglyceride accumulation in skeletal muscle and adipocytes, which impairs GLUT4 translocation (Storlien et al., 1996), and reduction of the number of insulin receptors in adipocytes (Harris, 1992; Harris and Kor, 1992). However, weaned non-ruminant animal species, such as pigs and rodents, do not commonly develop insulin resistance, although they consume higher amounts of glucose (but less fat) than veal calves. We therefore hypothesize that, apart from species differences, interactions between fatty acids and glucose may play a role in perturbing glucose homeostasis and inducing the development of insulin resistance in veal calves.
Standardized studies in which lactose and fat are exchanged (iso-energetically) may reveal the contribution of the dietary energy source to the development of insulin resistance in calves. The objective of the current study was therefore to assess effects of a large iso-energetic exchange of lactose and fat intake on insulin sensitivity in veal calves.
2.2 Materials and Methods
2.2.1 Animals and housing
Sixteen male Holstein-Friesian calves (120 ± 2.8 kg of BW; 99 ± 2.0 days old) were purchased. During the first 6 weeks of the 13-week study, calves were housed in pens of 4 calves each (2 m2 per calf) that were fitted with a wooden slatted floor and galvanized
fencings. Calves were then transferred to metabolic cages (dimension: 0.80 × 1.8 m) for the next 7 weeks. During this period, several measurements were performed (see Experimental Procedures). Ventilation occurred by ceiling fans, and illumination was by natural light and artificial (fluorescent lamps) light between 0700 and 1900 h. Temperature was controlled at 18°C and humidity at 65%.
Experimental procedures complied with the Dutch Law on Experimental Animals and the ETS123 (Council of Europe 1985 and the 86/609/EEC Directive) and were approved by the Animal Care and Use Committee of Wageningen University.
2.2.2 Experimental design, diets, and feeding
Calves were assigned to either a high-lactose diet (HL; n = 8) or a high-fat diet (HF; n
= 8), and to 1 of 8 blocks (pairs of calves) with 1 HL calf and 1 HF calf per block. Body weight and age did not differ between treatments at the start of the trial. Because of health problems in 2 HF calves, block 7 consisted of 2 HL calves, and block 8 (with the 2 remaining HF calves) was not included in the insulin sensitivity, insulin secretion, postprandial blood metabolites, and urinary glucose excretion measurements. Between treatments (Table 2.1), fat and lactose were exchanged iso-energetically based on digestible energy. Energy values of 39.0 kJ/g fat and 16.5 kJ/g lactose and ileal digestibilities of 95% for fat and 94% for lactose were assumed (Hof, 1980). High-lactose diet calves received 25% more feed than HF calves to obtain iso-energetic and
