56 Revisiting the adipocyte number hypothesis and its implications for developmental programming
Bibliographic record
Abstract
Abstract The strongest predictor of childhood obesity is being born to a mother who was obese during pregnancy, which is now the case for ∼30% of pregnancies in Canada and the USA. Therefore, women’s metabolic health during pregnancy is an important target for preventative measures aimed at addressing the obesity crisis. Our lab is exploring the role of adipose progenitors in programming early life adiposity and mediating the heightened risk for metabolic syndrome in offspring born to pregnancies complicated by obesity. The setpoint of adiposity is determined in early life by the number of adipocytes arising from a developmental pool of adipose progenitors that are specified to the adipocyte fate prior to birth. After puberty, adipocyte numbers remain stable throughout the rest of life. A pool of adipose progenitors residing in adult adipose depots serves as a reservoir to support adipocyte turnover, which is increased in obesity, and thereby plays a critical role in preserving metabolic health. Using a mouse model of diet-induced maternal obesity, our lab has shown that a perturbation in developmental adipogenesis, raises the setpoint of early life adiposity and predisposes to later-life adipose dysfunction and metabolic syndrome. Predisposition of offspring to metabolic dysfunction is sex-dependent, which our data demonstrate to be an estrogen-mediated sex difference arising at puberty rather than a programming effect. Together, our work provides new insight into the relationship between maternal obesity and childhood metabolic health and the modifying influence of sex. (Funded by the Canadian Institutes of Health Research, Heart and Stroke Foundation of Canada, Natural Sciences and Engineering Research Council of Canada, and Diabetes Canada)
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".