0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Oversupplying metabolizable protein during late gestation to beef cattle does not influence ante- or postpartum glucose-insulin kinetics but does affect prepartum insulin resistance indices and colostrum insulin content

      1 , 2 , 1 , 1
      Journal of Animal Science
      Oxford University Press (OUP)

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The objective of this study was to evaluate whether oversupplying metabolizable protein (MP) during late gestation influences glucose and insulin concentrations, and insulin resistance (IR) in late gestation and early lactation. Crossbred Hereford, first-lactation heifers were individually fed diets to supply 133% (HMP, n = 11) or 100% (CON, n = 10) of their predicted MP requirements for 55 ± 4 d (mean ± SD) prior to calving. All heifers received a common lactation ration formulated to meet postpartum requirements (103% MP and 126% ME). After feed was withheld for 12 h, cattle underwent an intravenous glucose tolerance test (IVGTT) on days −6.7 ± 0.9 and 14.3 ± 0.4 by infusing a 50% dextrose solution (1.36 g glucose/kg BW0.75) through a jugular catheter with plasma collected at −10, 0 (immediately after infusion), 5, 10, 15, 20, 25, 30, 45, 60, 75, 90, and 120 min, respective to the infusion. Glucose and insulin concentrations were assessed. Insulin resistance indices (homeostasis model of insulin resistance [HOMA-IR], quantitative insulin sensitivity check index [QUICKI], revised quantitative insulin sensitivity check index [RQUICK], and RQUICKI incorporating serum beta-hydroxybutyrate concentrations [RQUICKIBHB]) were calculated from measurements of serum non-esterified fatty acids and beta-hydroxybutyrate and plasma glucose and insulin concentrations on days −34 ± 4, −15 ± 4, 7 ± 1, 28 ± 3, 70 ± 3, and 112 ± 3. Colostrum samples were collected within an hour of calving (prior to suckling) and analyzed for insulin concentration. Data were analyzed as a randomized block design using the PROC GLIMMIX of SAS, accounting for repeated measurements when necessary. Baseline (−10 min) plasma glucose and insulin concentrations were elevated (P ≤ 0.038) for HMP heifers during the antepartum IVGTT, but not (P ≥ 0.25) during the postpartum IVGTT. Plasma glucose and insulin concentrations throughout the antepartum or postpartum IVGTT did not differ (P ≥ 0.18) by prepartum treatment, nor did other glucose and insulin IVGTT parameters (i.e., max concentration and time to reach max concentration, nadir values, clearance rates and half-lives, area-under-the-curve, and insulin sensitivity index; P ≥ 0.20). Antepartum IVGTT IR indices indicated that HMP heifers were more (P ≤ 0.011) IR than their counterparts. Similarly, the prepartum HOMA-IR was greater (P = 0.033) for HMP heifers, suggesting increased IR. Postpartum IR indices did not (P ≥ 0.25) indicate that prepartum MP consumption impacted postpartum IR. Colostrum insulin concentration was increased (P = 0.004) by nearly 2-fold for HMP relative to CON heifers. These data demonstrate that prepartum MP overfeeding alters baseline glucose-insulin concentrations in late-pregnant beef heifers and increases colostrum insulin content without having carry-over effects on postpartum glucose-insulin concentrations and IR.

          Related collections

          Most cited references72

          • Record: found
          • Abstract: found
          • Article: not found

          Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans.

          Insulin resistance plays an important role in the pathophysiology of diabetes and is associated with obesity and other cardiovascular risk factors. The "gold standard" glucose clamp and minimal model analysis are two established methods for determining insulin sensitivity in vivo, but neither is easily implemented in large studies. Thus, it is of interest to develop a simple, accurate method for assessing insulin sensitivity that is useful for clinical investigations. We performed both hyperinsulinemic isoglycemic glucose clamp and insulin-modified frequently sampled iv glucose tolerance tests on 28 nonobese, 13 obese, and 15 type 2 diabetic subjects. We obtained correlations between indexes of insulin sensitivity from glucose clamp studies (SI(Clamp)) and minimal model analysis (SI(MM)) that were comparable to previous reports (r = 0.57). We performed a sensitivity analysis on our data and discovered that physiological steady state values [i.e. fasting insulin (I(0)) and glucose (G(0))] contain critical information about insulin sensitivity. We defined a quantitative insulin sensitivity check index (QUICKI = 1/[log(I(0)) + log(G(0))]) that has substantially better correlation with SI(Clamp) (r = 0.78) than the correlation we observed between SI(MM) and SI(Clamp). Moreover, we observed a comparable overall correlation between QUICKI and SI(Clamp) in a totally independent group of 21 obese and 14 nonobese subjects from another institution. We conclude that QUICKI is an index of insulin sensitivity obtained from a fasting blood sample that may be useful for clinical research.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage.

            Insulin resistance contributes to the pathophysiology of diabetes and is a hallmark of obesity, metabolic syndrome, and many cardiovascular diseases. Therefore, quantifying insulin sensitivity/resistance in humans and animal models is of great importance for epidemiological studies, clinical and basic science investigations, and eventual use in clinical practice. Direct and indirect methods of varying complexity are currently employed for these purposes. Some methods rely on steady-state analysis of glucose and insulin, whereas others rely on dynamic testing. Each of these methods has distinct advantages and limitations. Thus, optimal choice and employment of a specific method depends on the nature of the studies being performed. Established direct methods for measuring insulin sensitivity in vivo are relatively complex. The hyperinsulinemic euglycemic glucose clamp and the insulin suppression test directly assess insulin-mediated glucose utilization under steady-state conditions that are both labor and time intensive. A slightly less complex indirect method relies on minimal model analysis of a frequently sampled intravenous glucose tolerance test. Finally, simple surrogate indexes for insulin sensitivity/resistance are available (e.g., QUICKI, HOMA, 1/insulin, Matusda index) that are derived from blood insulin and glucose concentrations under fasting conditions (steady state) or after an oral glucose load (dynamic). In particular, the quantitative insulin sensitivity check index (QUICKI) has been validated extensively against the reference standard glucose clamp method. QUICKI is a simple, robust, accurate, reproducible method that appropriately predicts changes in insulin sensitivity after therapeutic interventions as well as the onset of diabetes. In this Frontiers article, we highlight merits, limitations, and appropriate use of current in vivo measures of insulin sensitivity/resistance.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              What is the proper way to apply the multiple comparison test?

              Multiple comparisons tests (MCTs) are performed several times on the mean of experimental conditions. When the null hypothesis is rejected in a validation, MCTs are performed when certain experimental conditions have a statistically significant mean difference or there is a specific aspect between the group means. A problem occurs if the error rate increases while multiple hypothesis tests are performed simultaneously. Consequently, in an MCT, it is necessary to control the error rate to an appropriate level. In this paper, we discuss how to test multiple hypotheses simultaneously while limiting type I error rate, which is caused by α inflation. To choose the appropriate test, we must maintain the balance between statistical power and type I error rate. If the test is too conservative, a type I error is not likely to occur. However, concurrently, the test may have insufficient power resulted in increased probability of type II error occurrence. Most researchers may hope to find the best way of adjusting the type I error rate to discriminate the real differences between observed data without wasting too much statistical power. It is expected that this paper will help researchers understand the differences between MCTs and apply them appropriately.
                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Journal of Animal Science
                Oxford University Press (OUP)
                0021-8812
                1525-3163
                May 01 2022
                May 01 2022
                March 30 2022
                May 01 2022
                May 01 2022
                March 30 2022
                : 100
                : 5
                Affiliations
                [1 ]Department of Animal Biosciences, Ontario Agricultural College, University of Guelph, Guelph, ON N1G 2W1, Canada
                [2 ]Department of Animal and Poultry Science, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada
                Article
                10.1093/jas/skac101
                95e9c38c-e0aa-4e0f-86f9-0ff054c0eeb8
                © 2022

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

                History

                Comments

                Comment on this article