The potential mechanistic link between allergy and obesity development and infant formula feeding
Bibliographic record
Abstract
This article provides a new view of the cellular mechanisms that have been proposed to explain the links between infant formula feeding and the development of atopy and obesity. Epidemiological evidence points to an allergy- and obesity-preventive effect of breastfeeding. Both allergy and obesity development have been traced back to accelerated growth early in life. The nutrient-sensitive kinase mTORC1 is the master regulator of cell growth, which is predominantly activated by amino acids. In contrast to breastfeeding, artificial infant formula feeding bears the risk of uncontrolled excessive protein intake overactivating the infant's mTORC1 signalling pathways. Overactivated mTORC1 enhances S6K1-mediated adipocyte differentiation, but negatively regulates growth and differentiation of FoxP3(+) regulatory T-cells (Tregs), which are deficient in atopic individuals. Thus, the "early protein hypothesis" not only explains increased mTORC1-mediated infant growth but also the development of mTORC1-driven diseases such as allergy and obesity due to a postnatal deviation from the appropriate axis of mTORC1-driven metabolic and immunologic programming. Remarkably, intake of fresh unpasteurized cow's milk exhibits an allergy-preventive effect in farm children associated with increased FoxP3(+) Treg numbers. In contrast to unprocessed cow's milk, formula lacks bioactive immune-regulatory microRNAs, such as microRNA-155, which plays a major role in FoxP3 expression. Uncontrolled excessive protein supply by formula feeding associated with the absence of bioactive microRNAs and bifidobacteria in formula apparently in a synergistic way result in insufficient Treg maturation. Treg deficiency allows Th2-cell differentiation promoting the development of allergic diseases. Formula-induced mTORC1 overactivation is thus the critical mechanism that explains accelerated postnatal growth, allergy and obesity development on one aberrant pathway.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".