Maternal BMI and serum leptin concentration of infants in the first year of life1
Bibliographic record
Abstract
AIM: To evaluate the relationship between serum leptin concentration in infants and infant and maternal BMI. METHODS: 75 healthy term infants aged 0-12 mo, 51 exclusively breastfed (BF) for at least 4 mo and 24 formula fed (FF), were included in a cross-sectional study. Weight, length and cranial circumference of infants and their mothers were measured, body mass index (BMI) was calculated using the formula weight (kg)/height (m2) and infant serum leptin concentration was determined. RESULTS: A significant positive correlation between infant serum leptin concentration and maternal BMI was observed in BF infants (r = 0.389, p = 0.005), confirmed in a multiple regression model adjusted for infant age and infant BMI (beta = 0.065, p = 0.006), but not in FF infants. As regards the first 4 mo of life, we have already observed a positive correlation between infant serum leptin concentration and maternal BMI in BF infants (beta = 0.113, p = 0.005), but not in FF infants. CONCLUSION: In BF infants, serum leptin concentration correlates positively with maternal BMI. Higher maternal BMI values might increase leptin levels in breast milk, and maternal adiposity could be involved in infant energy balance.
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 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 | 0.003 |
| 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".