Maternal characteristics, milk-borne IGF-1, and neonatal growth: Insights into endocrine and developmental programming
Bibliographic record
Abstract
Background: Human milk contains a dynamic array of bioactive hormones and growth factors that extend beyond nutrition to influence neonatal growth, metabolism, and developmental programming. Among these, insulin-like growth factor-1 (IGF-1) is a pivotal mediator of tissue anabolism, gut maturation, and postnatal adaptation. Maternal metabolic and obstetric factors modify the concentration of IGF-1 and related hormones in milk, shaping infant growth trajectories from birth through early childhood. Objectives To examine how maternal characteristics—including body mass index (BMI), adiposity, gestational diabetes mellitus (GDM), and delivery mode—affect IGF-1 and associated milk hormones (insulin, leptin, adiponectin, ghrelin). To evaluate the impact of milk-borne IGF-1 on neonatal, preterm, and early-childhood growth outcomes. To explore mechanistic pathways linking maternal endocrine status, milk hormonal composition, and infant developmental programming. Methods: A structured literature search was performed in PubMed, Scopus, and Web of Science through March 2025. Eligible studies included human cohorts, case–control, and randomized trials reporting milk IGF-1 levels in relation to maternal factors or infant outcomes. Data extraction included sample characteristics, timing of milk collection, hormonal assays, and growth indices. Study quality was assessed using the Newcastle–Ottawa Scale and Cochrane RoB-2 tools. Results were synthesized descriptively due to heterogeneity across designs. Results: Twenty-two studies met inclusion criteria. Maternal obesity and diabetes were consistently associated with elevated milk IGF-1 and insulin but reduced adiponectin and obestatin, enhancing early postnatal weight gain. Cesarean delivery and social stress were linked to lower IGF-1 levels, while early breastfeeding in preterms significantly increased serum IGF-1 and promoted catch-up growth. Experimental supplementation with enteral IGF-1 improved intestinal integrity but did not accelerate weight gain. Longitudinal cohorts revealed a biphasic effect: higher early milk IGF-1 correlated with increased infant weight at 1 year but reduced BMI at 3–5 years, reflecting adaptive metabolic programming. Pasteurization of donor milk decreased IGF-1 bioactivity by ~40%, underscoring the benefit of mother’s own milk. Conclusions: Maternal metabolic health, nutritional status, and perinatal factors critically determine milk IGF-1 bioavailability and its impact on neonatal growth. Early exposure to milk-borne IGF-1 supports gut and somatic development, particularly in preterm infants, while long-term effects suggest homeostatic regulation of adiposity. Optimizing maternal diet, glucose control, and lactation practices may enhance IGF-1 concentrations and confer lasting benefits on child growth and metabolic outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".