Drosophila as a useful model for understanding the evolutionary physiology of obesity resistance and metabolic thrift
Bibliographic record
Abstract
Evolved metabolic thriftiness in humans is a proposed contributor to the obesity epidemic. Insect models have been shown to evolve both ‘metabolic thrift’ in response to rearing on high-protein diets that promote leanness, and ‘obesity resistance’ when reared on fattening high-carbohydrate, low-protein foods. Despite the hypothesis that human obesity is caused by evolved metabolic thrift, genetic contributions to this physiological trait remain elusive. Here we conducted a pilot study to determine whether thrift and obesity resistance can arise under laboratory based ‘quasi-natural selection’ in the genetic model organism Drosophila melanogaster. We found that both these traits can evolve within 16 generations. Contrary to predictions from the ‘thrifty genotype/phenotype’ hypothesis, we found that when animals from a metabolic thrift inducing high-protein environment are mismatched to fattening high-carbohydrate foods, they did not become ‘obese’. Rather, they accumulate less triglyceride than control animals, not more. We speculate that this may arise through as yet un-quantified parental effects – potentially epigenetic. This study establishes that D. melanogaster could be a useful model for elucidating the role of the trans- and inter-generational effects of diet on the genetics of metabolic traits in higher animals.
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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) | 0.002 | 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".