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Record W4396651716 · doi:10.1093/jas/skae102.216

239 Variation in muscle protein synthesis in growing gilts is partially associated with insulin resistance and sensitivity

2024· article· en· W4396651716 on OpenAlexaff
Aline Remus, H. Lapierre, Marie‐France Palin, C. Pomar

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsVariation (astronomy)Insulin sensitivitySensitivity (control systems)Muscle proteinInsulin resistanceInternal medicineInsulinEndocrinologyChemistryBiologyMedicineSkeletal musclePhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Pigs with similar body weight (BW), same genetic background and sex vary in protein deposition (PD) overgrowth. We aimed to study metabolic factors influencing PD in 65 kg BW gilts. Gilts were fed ad libitum a diet balanced to maximize PD. Dual X-rays were conducted to estimate body protein mass on d 1 and 21, allowing calculation of PD by difference. On d 23, the gilts were categorized into Low (157 g/d) and High (219 ± 9.97 g/d) PD groups. In each group, eight pigs underwent jugular vein cannulation. After a 48-h recovery period, where they were fed at 90% of the measured feed intake, they were fasted for 8 h. All pigs were then given a 300 g meal at time 0, and blood samples were collected at 10, 15, 20, 30, 40, 50, 60, 75, 90, 120, 150, 178, and 178 min, to study insulin and glucose responses. At 180 min post-meal, a solution of L-13C valine was administered i.v. (18 ape – flooding dose), and liver, duodenum, and longissimus dorsi muscle samples were collected within 5 minutes of slaughter. The fractional synthesis rate (FSR, %/d), absolute synthesis rate (ASR, g/d), ribosomal capacity for protein synthesis (Cs), and the efficiency of protein synthesis (KRNA) based on total RNA concentration in skeletal muscle were calculated. The insulin resistance index (HOMA-B) and insulin sensitivity index (QUICKI) were also determined. The difference between Low and High PD gilts was compared using a F-test. The longissimus dorsi muscle exhibited a 31% greater (P = 0.01) FSR in High compared with Low PD group. No significant differences were observed in the FSR of the jejunum and liver. High PD gilts showed a 55% greater ASR in whole-body muscles (P = 0.003) and 42% greater ASR in the liver (P = 0.04) compared with Low PD gilts, with no difference for the ASR in the jejunum. The KRNA in the longissimus dorsi was 48% greater in High PD gilts (P = 0.01), with no differences in KRNA for the jejunum or liver. Cs did not differ between Low and High PD gilts. The QUICKI index tended to be greater in High PD gilts (P = 0.10). Spearman’s correlation revealed that longissimus dorsi FSR was negatively correlated with the HOMA-B index (r = -0.53; P = 0.04). Increases in the HOMA-B index tended to be associated with greater KRNA (r = -0.49; P = 0.06). These correlations highlight the complex interplay of metabolic and hormonal factors involved in the observed differences in protein synthesis and efficiency in growing gilts. Insulin resistance and sensitivity may play a role in influencing skeletal muscle PD variations.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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