The Profile of Human Milk Metabolome, Cytokines, and Antibodies in Inflammatory Bowel Diseases Versus Healthy Mothers, and Potential Impact on the Newborn
Bibliographic record
Abstract
BACKGROUND AND AIMS: For women with inflammatory bowel disease [IBD], it is not very well known how IBD or IBD treatment affects their breast milk components. We aimed to investigate whether breast milk composition differs in healthy control [HC] versus IBD mothers in terms of antibodies, cytokines, and metabolite,s to identify potential impact of IBD breast milk on neonatal immune system. METHODS: Breast milk specimens from HC [n = 17] and IBD [n = 31 for Crohn's disease [CD]; and n = 41 for ulcerative colitis [UC]; were collected at 3 and 6 months postpartum [PP3] and [PP6], respectively. Faecal samples were also collected. Cytokines and immunoglobulins [IgA/IgG/IgE] were analysed by multiplex Meso Scale Discovery [MSD] and commercial kits. Moreover, breast milk metabolites were analysed by 1H nuclear magnetic resonance [NMR]. RESULTS: We found that breast milk from IBD mothers showed significantly lower levels of IgA, sugar metabolite [lactose], and 2-aminobutyrate. In contrast, we observed that breast milk from mothers with IBD had increased levels of pro-inflammatory cytokines and higher energy metabolites [lactate and succinate] than milk from healthy mothers. In addition, we noticed that the type of treatment [5-aminosalicylic acid versus biologics] influenced the milk cytokines and metabolites profile. CONCLUSIONS: The reduction in immunoprotective components of IBD breast milk such as sIgA and lactose theoretically may modulate the potential protective effects of breastfeeding. On the other hand, presence of higher levels of pro-inflammatory cytokines, lactate, and succinate may predispose the offspring to an inflammatory condition or impact on the gut microbiome. Better understanding of the role of succinate in infants and its potential effects on microbiome or mucosal immunity merits further investigations.
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 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 | 0.000 | 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 | 0.000 |
| 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".