Metabolic Adaptation to Feeding and Fasting during Lactation in Humans
Bibliographic record
Abstract
The aim of these studies was to determine the metabolic adaptation to fasting and feeding during lactation. Normal lactating (L) and nonlactating (NL) women (n = 6 each) were studied using infusions of [U-13C]glucose and [2-13C]glycerol during: 1) a 24-h fast, and 2) ingestion of Sustacal (protocol 1). In addition, 8 L and 6 NL women were studied during infusion of [6,6-2H2]glucose and ingestion of a glucose meal containing [1-13C]glucose (protocol 2). Protocol 1: Glucose production rate (GPR) during fasting was 33% higher in the L women (12.5 +/- 1.0 vs. 9.4 +/- 0.5 micromol x kg(-1) x min(-1); P < 0.03). Fractional gluconeogenesis (GNG), GNG rate, glucose, lactate, beta- hydroxybutyrate, FFA, insulin, and C-peptide were similar in both groups during feeding and fasting, but glycogenolysis was 50% higher in fasting L women. Protocol 2: Although GPR was slightly increased in the L group (L, 1.8 +/- 0.2 micromol x kg(-1) x min(-1); NL, 1.2 +/- 0.2 micromol x kg(-1) x min(-1); P < 0.04), no other differences were observed in splanchnic and systemic metabolism of ingested glucose between L and NL women. Insulin concentrations were lower in L women compared with controls (L, 15 +/- 3 microU/ml; NL, 28 +/- 6 microU/ml; P = 0.05). In conclusion, the increased glucose demands of lactation are met by increased GPR as a result of increased glycogenolysis but not GNG or by increased use of FFA. During feeding, lactating women handle oral carbohydrates normally but have increased insulin sensitivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".