The impact of multigenerational high-fat diet feeding on the gut microbiome and host metabolism
Bibliographic record
Abstract
Abstract Although human genetics partially explain the heritability of obesity and type 2 diabetes (T2D), the human gut microbiome also plays a significant role. While changes in human genetics at the population level occur only after many generations, the gut microbiome evolves over a shorter time span. The gut microbiome is also vertically transmitted across generations, therefore, changes in one generation can be passed on to subsequent generations. However, it is unknown whether the gut microbiome influences natural selection of its host under obesogenic pressure. Here, we show that C57BL/6 mice fed a high-fat diet (60% fat, HFD) over four generations develop resistance to obesity and the metabolic syndrome (MetS). Unexpectedly, the mice were increasingly leaner as well as more glucose tolerant and insulin sensitive across generations. This phenomenon was attributed to the most obese mice not yielding progenies, whereas the leanest mice successfully reproduced, and their offspring were also resistant to obesity. In other words, a population bottleneck was observed. Because all the mice were nearly genetically identical inbred C57BL/6J mice, the large variation in body weight gain in response to HFD feeding was likely independent of genetics. We explored whether microbial factors enriched in obesity-resistant mice promote healthier host metabolic phenotypes under HFD feeding, thereby contributing to the heterogeneity in body weight gain and providing an adaptive advantage to the host. Pearson correlation analysis revealed that body weight gain was positively correlated with Lactococcus lactis , as well as negatively correlated with Lactobacillus johnsonii and pathways for coenzyme A biosynthesis, amino acid biosynthesis (lysine, isoleucine, valine), and nucleotide biosynthesis (adenosine, guanosine). Overall, we observed multigenerational adaptation in the gut microbiome correlated with improved metabolism, yet further studies are needed to validate that these adaptations drive metabolic health.
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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".