Adipocyte size as a determinant of metabolic disease and adipose tissue dysfunction
Bibliographic record
Abstract
Obesity is a heterogeneous disease and is associated with comorbidities such as type 2 diabetes mellitus, cardiovascular disease and cancer. Several studies have examined the role of dysfunctional adipose tissue in the pathogenesis of obesity, highlighting the contrasting properties and impact of distinct fat compartments, sometimes with contradictory results. Dysfunctional adipose tissue involves enlargement, or hypertrophy, of pre-existing fat cells, which is thought to confer increases in cardiometabolic risk, independent of the level of obesity per se. In this article, we critically analyze available literature that examined the ability of adipocyte cell size to predict metabolic disease and adipose tissue dysfunction in humans. Many studies demonstrate that increased fat cell size is a significant predictor of altered blood lipid profiles and glucose-insulin homeostasis independent of adiposity indices. The contribution of visceral adiposity to these associations appears to be of particular importance. However, available studies are not unanimous and many fat depot-specific aspects of the relationship between increased fat cell size and cardiometabolic risk or parameters of adipose tissue dysfunction are still unresolved. Methodological factors such as the approach used to express the data may represent significant confounders in these studies. Additional studies should consider the fact that the relationship between fat cell size and common adiposity indices is non-linear, particularly when reaching the obese range. In conclusion, our analysis demonstrates that fat cell size is a significant predictor of the cardiometabolic alterations related to obesity. We propose that adipocyte hypertrophy, especially in the visceral fat compartment, may represent a strong marker of limited hyperplasic capacity in subcutaneous adipose tissues, which in turn is associated with the presence of numerous cardiometabolic alterations.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".