Choline is required in the diet of lactating dams to maintain maternal immune function
Bibliographic record
Abstract
Choline demands during lactation are high; however, detailed knowledge is lacking regarding the optimal dietary intake during this critical period. The present study was designed to determine the effects of varying intakes of choline on maternal immune function during lactation. Primiparous Sprague-Dawley rats (n 42) were randomised 24-48 h before birth and fed the following diets for 21 d: choline-devoid (0 g choline/kg diet; D, n 10); 1·0 g choline/kg diet (C1, n 11); 2·5 g choline/kg diet (C2·5, n 10); 6·2 g choline/kg diet (C6, n 11). Splenocytes were isolated and stimulated ex vivo with concanavalin A, lipopolysaccharide (LPS) or CD3/CD28. D and C6 dams had lower final body weight, spleen weight and average pup weight than C1 dams (P< 0·05). There was a linear relationship between free choline concentration in pup stomach contents with maternal dietary choline content (P< 0·001, r² 0·415). Compared with C1 and C2·5, D spleens had a lower proportion of mature T cells and activated suppressor cells, and this resulted in reduced cytokine production after stimulation (P< 0·05). Feeding 6·2 g choline/kg diet resulted in a higher cytokine production after stimulation with CD3/CD28 (P< 0·05). Except for a higher IL-6 production after LPS stimulation with cells from the C2·5 dams (P< 0·05), there were no differences between the C1 and C2·5 dams. For the first time, we show that feeding lactating mothers a diet free of choline has substantial effects on their immune function and on offspring growth. Additionally, excess dietary choline had adverse effects on maternal and offspring body weight but only minimal effects on maternal immune function.
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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 | 0.000 |
| 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 | 0.001 |
| 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".