Maternal obesity increases the risk of hepatocellular carcinoma through the transmission of an altered gut microbiome
Bibliographic record
Abstract
Background & Aims: Emerging evidence suggests that maternal obesity negatively impacts the health of offspring. Additionally, obesity is a risk factor for hepatocellular carcinoma (HCC). Our study aims to investigate the impact of maternal obesity on the risk for HCC development in offspring and elucidate the underlying transmission mechanisms. Methods: Female mice were fed either a high-fat diet (HFD) or a normal diet (ND). All offspring received a ND after weaning. We studied liver histology and tumor load in a N-diethylnitrosamine (DEN)-induced HCC mouse model. Results: 0.0089), which was rescued through co-housing. In the principal component analysis, the microbiota profile of co-housed animals clustered together, regardless of maternal diet. Co-housing of HFD offspring with ND offspring normalized their tumor load. Conclusions: Maternal obesity increases female offspring's susceptibility to HCC. The transmission of an altered gut microbiome plays an important role in this predisposition. Impact and implications: The worldwide incidence of obesity is constantly rising, with more and more children born to obese mothers. In this study, we investigate the impact of maternal diet on gut microbiome composition and its role in liver cancer development in offspring. We found that mice born to mothers with a high-fat diet inherited a less diverse gut microbiome, presented chronic liver injury and an increased risk of developing liver cancer. Co-housing offspring from normal diet- and high-fat diet-fed mothers restored the gut microbiome and, remarkably, normalized the risk of developing liver cancer. The implementation of microbial screening and restoration of microbial diversity holds promise in helping to identify and treat individuals at risk to prevent harm for future generations.
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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 | 0.000 | 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".