Maternal and neonatal red blood cell n-3 polyunsaturated fatty acids inversely associate with infant whole-body fat mass assessed by dual-energy X-ray absorptiometry
Bibliographic record
Abstract
Research regarding polyunsaturated fatty acid (PUFA) status and body composition in neonates is limited. This study tested the relationship between newborn docosahexaenoic acid (DHA) status and body composition. Healthy mothers and their term-born infants (n = 100) were studied within 1 month postpartum for anthropometry and whole-body composition using dual-energy X-ray absorptiometry. Maternal and infant red blood cell (RBC) membrane PUFA profiles were measured using gas chromatography (expressed as percentage of total fatty acids). Data were grouped according to infant RBC DHA quartiles and tested for differences in n-3 status and infant body composition using mixed-model ANOVA, Spearman correlations, and regression analyses (P < 0.05). Mothers were 32.2 ± 4.6 years (mean ± SD) of age, infants (54% males) were 0.68 ± 0.23 month of age, and 80% exclusively breastfed. Infant RBC DHA (ranged 3.96% to 7.75% of total fatty acids) inversely associated with infant fat mass (r = –0.22, P = 0.03). Infant and maternal RBC n-6/n-3 PUFA ratio (r2 = 0.28, P = 0.043; r2 = 0.28, P = 0.041 respectively) were positively associated with fat mass. These results demonstrate that both maternal and infant long-chain PUFA status are associated with neonatal body composition. Novelty Our findings support an early window to further explore the relationship between infant n-3 PUFA status and body composition. Maternal and infant n-3 PUFA status is inversely related to neonatal whole-body fat mass. DHA appears to be the best candidate to test in the development of a lean body phenotype.
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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 | 0.001 | 0.002 |
| 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".