Gender differences in the risk of developing obesity related disease can be explained by sex‐specific molecular signatures that regulates adipose endothelial cell growth
Bibliographic record
Abstract
Obesity is the excessive growth of adipose tissue resulting from the chronic consumption of excess calories and is a recognised risk factor for developing cardio-metabolic diseases. There is however a significant variation in the size and expandibility of different adipose tissue in humans. The inability of adipose tissue to expand “safely” leads to ectopic fat deposition, causing insulin resistance, type 2 diabetes and cardiovascular conditions – which are more prevalent in men than women. Thus, the mechanisms that control the expandability of adipose tissue are key factors in determining diabetes risk in obesity. An interesting study from York University have provided new insights on the biological underpinning in gender differences in the risk of developing obesity-related diseases. Previous study from this group has observed that when mice become obese, females grow more new blood vessels to supply the expanding fat tissue with oxygen and nutrients, whereas males grow a lot less. To understand the mechanism regulating this, the team used software to help sift through thousands of genes to zero in on the ones that would be associated with blood vessel growth. They discovered that processes associated with the proliferation of new blood vessels were high in the female mice, whereas the males had a high level of processes associated with inflammation. Thereafter, the investigators examined the behaviour of the endothelial cells ex-vivo. Interestingly, they observed that even when these cells were studied ex-vivo, male and female endothelial cells still behave very differently from each other - female endothelial cells replicated faster, while male endothelial cells displayed greater sensitivity to an inflammatory stimulus. Further studies to investigate the effects of specific genes in humans that regulate endothelial cells growth will provide potential therapeutic target to manage and treat obesity related cardio-metabolic diseases. The study was funded by a grant through the Canadian Institutes of Health Research, as well as the Natural Science and Engineering Research Council of Canada and York's Faculty of Health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".