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* Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg 24061-0306 and
and
Equine Studies Group, WALTHAM Centre for Pet Nutrition, Melton Mowbray, U.K.
2 Correspondence:
Virginia Tech MARE Center, 5527 Sullivans Mill Rd., Middleburg, VA 20117 (phone: 540-687-3521; fax: 540-687-5362; E-mail:
Rhonda.Hoffman{at}vt.edu).
| Abstract |
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Key Words: Fat Glucose Tolerance Test Lactation Mares Pregnancy
| Introduction |
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A similar procedure, the glycemic index, measures plasma glucose and insulin responses to a meal and provides information about the food but not necessarily the animal. The glycemic index has been applied primarily in human nutrition for diabetics in order to formulate diets with a low glycemic impact (Wolever et al., 1991). In horse nutrition, the glycemic index has been used to describe meal-related responses of blood glucose and insulin to different diets (Stull and Rodiek, 1988; Williams et al., 2001). Factors affecting glycemic index include meal size, concentrations of hydrolyzable carbohydrates, fiber and fat, processing, intake time, gastric emptying, digestibility, and rate of absorption.
Glucose metabolism adapts to pregnancy and lactation in many species (Bell, 1995; Boden, 1996), including horses (Evans, 1971). The objective of this study was to determine the effect of physiological state (gestation and lactation) and supplemental dietary energy source (sugar and starch or fiber and fat) on glucose metabolism in grazing mares, as assessed using an oral glucose tolerance test. The hypothesis was twofold: 1) the glycemic response to oral glucose will be greater during gestation than lactation, and 2) because the oral glucose load would be similar to a meal rich in sugar and starch, the largest glycemic response would be noted in mares not adapted to sugar and starch.
| Materials and Methods |
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Supplements
In midgestation, the mares were paired by age, breeding date, and sire of their foal, and then randomly assigned into two groups. In addition to pasture, 10 mares and their foals were fed a corn grain and molasses supplement (SS) and 10 were fed a corn oil and fiber supplement (FF). The supplements (Table 1
) were formulated to be isocaloric and isonitrogenous, with mineral contents balanced to complement the pasture and to meet or exceed current recommendations (NRC, 1989). The supplements were fed to the mares in varying amounts, with goals of approximately a 1:2 supplement:forage ratio (Kronfeld, 1998) and BCS of 5 and 6 for mares, using a scale of 1 to 9 (Henneke et al., 1983). The amounts fed were 2.9, 4.0, and 3.5 kg/d per mare in late gestation, early lactation, and late lactation, respectively, divided into two meals. The supplements were fed in pans placed in a circular pattern on the ground with a distance of approximately 6 m between pans to minimize dominant/submissive behavioral effects during feed ingestion. The mares were observed to ensure each consumed their allotted amount of supplement.
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For each oral glucose tolerance test, the mares were weighed using an electronic scale (Tyrel Platform, model TC-10S, Allweights Hamilton Scale Corp., Richmond, VA) and moved from pastures into stalls 15 to 18 h before the onset of the test. Mares from the same groups were housed so they could see each other in order to avoid social dislocative stress. Fasting has been shown to reduce tissue sensitivity to the glucoregulatory action of insulin in equids (Forhead and Dobson, 1997), and because the effect of fasting on insulin resistance may be exacerbated by pregnancy (Fowden et al., 1984), the mares were allowed ad libitum access to grass hay and water in order to mimic the nonfasted, grazing state on pasture.
Catheters were placed in jugular veins, and after an adjustment period of approximately 1 h, baseline blood samples were taken at 30 min and 1 min before the glucose challenge. Each mare was given a glucose dose of 0.2 g/kg of BW in a 50% solution via nasogastric tube. This dose is lower than that reported in other studies (Roberts and Hill, 1973; Jacobs and Bolton, 1982) but was chosen because it was calculated to provide a glucose load similar to that provided by the hydrolyzable carbohydrate in a 0.5-kg meal of heavy oat grain (IFN 4-18-520).
Venous samples were collected at 30, 60, 90, 120, 150, 180, 240, and 300 min after the glucose challenge. The blood samples were immediately placed in heparinized sample tubes (Vacutainer, Fisher Health Care, Chicago, IL), kept in ice water, centrifuged, and then plasma was removed within 10 to 20 min of collection. Plasma was frozen at -4°C pending analysis.
Analyses
Samples of supplements, pastures, and hay were submitted for proximate and mineral analysis (Dairy One DHIA Forage Testing Laboratory). Nonstructural carbohydrate of the supplements and forage samples was calculated by difference from the proximate analysis. Samples were also analyzed for hydrolyzable carbohydrate using direct methods (Smith, 1981; Hoffman et al., 2001).
Plasma glucose concentrations were determined by colorimetric assay (Glucose Procedure #16-UV, Sigma Diagnostics, St. Louis, MO), and insulin and cortisol were determined using radioimmunoassays (Coat-A-Count insulin, Coat-A-Count cortisol, Diagnostic Products, Los Angeles, CA) previously validated for equine insulin and cortisol (Freestone et al., 1991). Duplicate assays had an intraassay CV of <1% for glucose, 5% for insulin, and 4% for cortisol. The interassay CV was 2% for glucose, 5.5% for insulin, and 4% for cortisol.
Glucose, insulin, and cortisol data were summarized as least squares means and standard errors and plotted over time. The magnitude of each glucose or insulin response was calculated as the incremental area under the curve (AUC) by graphical approximation. The AUC was negligible for cortisol concentrations. Glucose clearance was calculated as dose (g/kg of BW) divided by AUC (gminL-1).
Statistical Analysis
The data were tested for normality by the Shapiro-Wilk statistic. Data did not fit a normal distribution for glucose clearance (P = 0.0001) or for insulin AUC (P = 0.0027), so log10 transformations were applied and used for statistical analysis. Log10 transformations were also used for statistical analysis of insulin AUC:glucose AUC ratios.
The pasture and hay samples were compared using an ANOVA mixed model with repeated measures, with individual pasture and collection months corresponding to reproductive stage as fixed effects (SAS Inst., Inc., Cary, NC). Plasma data were compared using an ANOVA mixed model with repeated measures, with diet and reproductive stage as fixed effects. Means from both forage and plasma mixed models were compared using the Tukey test. Results were considered statistically significant at P < 0.05 and as a trend toward statistical significance at 0.05 < P < 0.10 (Rosner, 1995).
| Results |
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Glucose
Basal glucose concentrations (Table 4
) were higher in SS than FF mares during early lactation (P = 0.030), but no diet effects were noted (P > 0.18) in late gestation or late lactation. For both diets, basal glucose concentrations were lower in early lactation than in late gestation (P = 0.001) or late lactation (P = 0.030). In FF mares, basal glucose was lower in early lactation than late gestation (P = 0.001) and late lactation (P = 0.002). In SS mares, basal glucose was lower in early lactation than late gestation (P = 0.005) and tended to be lower in late lactation than late gestation (P = 0.095).
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Insulin
Basal insulin concentrations (Table 4
) were not different (P > 0.14) between SS and FF mares during any reproductive stage. For both diets, basal insulin was higher in late gestation than early (P = 0.001) or late lactation (P = 0.001), and was lower in early lactation than late lactation (P = 0.001). In FF mares, basal insulin was higher in late gestation than early (P = 0.019) or late (P = 0.027) lactation. In SS mares, basal insulin was higher in late gestation than in early (P = 0.001) or late lactation (P = 0.001) and was lower in early lactation than late lactation (P = 0.001).
After oral glucose administration, the highest plasma insulin concentrations were measured at 30 min in the SS mares and 60 min in the FF mares, regardless of reproductive stage (Figure 2
). Plasma insulin concentrations were not different (P > 0.10) from baseline concentrations at 90 min in SS mares and 240 min in FF mares, and at 120 min in late gestation and early lactation, and 180 min in late lactation after oral administration of glucose.
The insulin AUC was influenced primarily by reproductive stage (Figure 1
; Table 5
). For both diets, insulin AUC was lower in early lactation than in late gestation (P = 0.002) or late lactation (P = 0.004). Insulin AUC was higher in FF vs. SS mares in late gestation (P = 0.045), but not in early or late lactation (P > 0.57). In SS mares, insulin AUC was lower in early lactation than in late gestation (P = 0.039) or late lactation (P = 0.010), and in FF mares, insulin AUC was lower in early lactation than in late gestation (P = 0.002) or late lactation (P = 0.013).
Insulin:Glucose Ratio
The log10 of the insulin AUC:glucose AUC ratio (Table 5
) was not influenced by reproductive stage or diet (P > 0.12).
Cortisol
Basal cortisol concentrations (Table 4
) were lower in SS vs. FF mares during early lactation (P = 0.013), but not (P = 0.81) during late gestation or late lactation. For both diets, basal cortisol was lower in late gestation than early lactation (P < 0.001) or late lactation (P = 0.016). In FF mares, basal cortisol was higher in early lactation than in late gestation (P = 0.001) or late lactation (P = 0.012), and higher in late lactation than in late gestation (P = 0.026). In SS mares, basal cortisol was lower in late gestation than in early (P = 0.005) or late lactation (P = 0.048). Plasma cortisol concentrations were not consistently influenced by oral administration of glucose.
| Discussion |
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Because of previous confounding of glucose tolerance and glycemic index in equine studies, it should be emphasized that the present investigation evaluated chronic adaptations of mares to feed energy sources by means of the glucose tolerance or clearance test (rather than the glycemic effect of a meal). Previous reports have used a grain meal to which the horse is adapted as the stimulus to elicit a glucose and insulin response, in effect a glycemic index. The glycemic response to a grain meal may be influenced by the amount of hydrolyzable carbohydrate in the meal, mastication, intake time, and digestibility of the feed. The oral glucose tolerance test used in this study removed the variability associated with a grain meal because the specific glucose dose was known, and variation associated with intake was not a factor. The oral glucose dose used provided glucose equivalent to that expected in a proportionately small grain meal, an amount to which the SS mares would be adapted but which would be unique to the FF mares. The higher glucose AUC in the FF mares indicated that the FF mares were unaccustomed to twice-daily glucose and insulin perturbations associated with meal feeding of SS.
Previous work in our laboratory (Williams et al., 2001) using SS and FF supplements similar to those used in this study indicated a higher feed glycemic index, assessed as higher glucose and insulin AUC, in the SS vs. FF supplement. A larger glycemic response to an oral glucose tolerance test would be expected in horses adapted to diets with a low glycemic index, such as FF or pasture, and a lower glycemic response to an oral glucose tolerance test would be expected in horses chronically perturbed twice daily by meals with a high glycemic index, such as SS or traditional sweet feeds. Similarly, horses adapted to pasture only, compared with a stable diet of hay and commercial feed, had a higher response to an oral glucose dose and 1.8 times as much glucose AUC (Jacobs and Bolton, 1982). Compared with pasture-fed horses, the horses fed the stable diet had approximately 3.5 times as much hydrolyzable carbohydrate, so their diet likely had a higher glycemic index, which influenced their response to oral glucose. Adaptation to meals with a high glycemic index may have enhanced the ability of these stabled horses, as well as the SS mares in this study, to clear glucose at a rate faster than horses accustomed to their inherent nature of grazing pasture.
The metabolic adaptation of the mares to pregnancy was reflected in larger glucose and insulin AUC and slower glucose clearance during late gestation compared with early lactation. This was more evident in the FF mares, whose adaptation to fatty acid utilization may have enhanced their ability to reserve glucose for fetal needs and to meet maternal energy requirements by peripheral metabolism of fatty acids. Adaptation to dietary fat in the horse has been found to enhance oxidation of long-chain fatty acids and reduce utilization of glucose and glycogen (Potter et al., 1992). In contrast to the FF mares, the adaptation to the high-glycemic index SS feed and a twice-daily need to accommodate hyperglycemia associated with meal feeding in the SS mares improved glucose clearance but could possibly affect glucose conservation for fetal needs, as alluded by the negligible effect of reproductive stage in SS mares. Further research would be required for verification.
Glucose metabolism adapts to pregnancy and lactation in many species, including humans (Boden, 1996), laboratory animals (Leturque et al., 1987), sheep (Petterson et al., 1993), cattle (Bell, 1995), pigs (Père et al., 2000), and horses (Evans, 1971; Fowden et al., 1984). The adaptation of glucose metabolism to pregnancy includes progressive development of insulin resistance, which allows for improved placental transfer of glucose in order to meet increasing demands of the fetus (Leturque et al., 1987; Petterson et al., 1993; Père et al., 2000). The insulin resistance facilitates the supply of glucose to the fetus at the expense of maternal tissues, through a shift in substrate utilizationfrom carbohydrates to fatty acids, and decreased glucose utilization in peripheral tissues (Bell, 1995; Boden, 1996). In horses, changes in pancreatic ß-cell function during pregnancy may contribute to insulin resistance (Fowden et al., 1984), which would elicit effects on glucose metabolism similar to those found in this study. In humans, progressive insulin resistance may trigger gestational diabetes, which increases risks of perinatal complications and subsequent development of maternal noninsulin-dependent diabetes mellitus.
One might consider that the effect of reproductive stage may have been confounded to some extent by adaptation to seasonal changes in pasture nutrients. Mares in late pregnancy consumed hay in combination with pasture, which may have increased their intake of ADF and NDF and lowered intake of hydrolyzable carbohydrate. The primary seasonal difference in pasture was higher NDF and lower nonstructural carbohydrate and hydrolyzable carbohydrate in early lactation compared with late gestation or late lactation. If this seasonal variation in carbohydrate composition were the primary influence on glucose metabolism, the expected outcome would have been a larger glucose and insulin AUC and slower glucose clearance during early lactation. Our results were not in agreement with this expected seasonal effect, so clearly the effect of reproductive stage dominated any effect of seasonal variation in forage nutrients.
The trend toward more rapid glucose clearance in SS mares during early lactation and the negligible effect of diet may reflect increased demands of the mammary gland for glucose as needed for milk synthesis compared with the demands of the gravid uterus. In dairy cattle, glucose uptake by the gravid uterus accounts for approximately one half of the maternal glucose supply, whereas the lactating mammary glucose requirement is estimated at three times that of the gravid uterus (Bell, 1995). Considering the substantial increase in glucose requirement for lactation, mostly for lactose synthesis, which is in greater concentration in mare milk than in cow milk (Ullrey et al., 1966; Naylor and Bell, 1985), it is likely that the increased utilization of glucose during early lactation was influenced by mammary demands.
| Implications |
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| Footnotes |
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Received for publication August 8, 2002. Accepted for publication March 6, 2003.
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