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Record W4221048578 · doi:10.1093/jas/skac101

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

2022· article· en· W4221048578 on OpenAlexaff
Koryn S Hare, G.B. Penner, M.A. Steele, Katharine M Wood

Bibliographic record

VenueJournal of Animal Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of SaskatchewanUniversity of Guelph
Fundersnot available
KeywordsInsulinInsulin resistanceInternal medicineEndocrinologyColostrumLactationQuantitative insulin sensitivity check indexGestationIce calvingBiologyAnimal scienceMedicinePregnancyInsulin sensitivity

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.252
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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