Diet induced maternal obesity affects offspring gut microbiota and persists into young adulthood
Bibliographic record
Abstract
Accumulating evidence suggests that diet could shape the host gut microbiome composition. Herein, we investigated the effects of maternal high fat diet (HFD) consumption on the gut microbiota and serum profile of mice offspring, and attempted to explore the beneficial roles of maternal probiotics intervention. Female C57BL/6J mice were fed with normal diet, HFD or HFD with daily probiotics (B. breve DM8310, L. acidophilus DM8302, L. casei DM8121 and S. thermophilus DM8309) by gavage starting 6 weeks prior to breeding and continued throughout gestation and lactation. Pups of HFD dams had higher levels of total cholesterol (TC), low-density lipoprotein (LDL), glucose, insulin and leptin compared to those of chow-fed dams. Maternal probiotics intervention resulted in a decrease in the lipid levels in all the pups, while the glucose, insulin and leptin levels were decreased only in adult female pups compared to those from HFD-fed dams; the decreased levels were similar to those in the pups of chow-fed dams. In line with these plasma changes, maternal HFD persistently altered the composition of the offspring gut microbiota in a sex specific way. Maternal probiotics intervention could ameliorate gut microbiota dysbiosis in the offspring. Such intervention showed better effects particularly for the female pups at adulthood. In conclusion, maternal HFD-induced gut microbiota dysbiosis and metabolic disorder could persist through the adulthood of the offspring. Maternal probiotics intervention can negate the detrimental effects of maternal HFD on the gut microbiota and metabolism in the offspring in a sex specific way.
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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 | 0.000 |
| 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 | 0.001 |
| 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".