Molecular insights into the role of white adipose tissue in metabolically unhealthy normal weight and metabolically healthy obese individuals
Bibliographic record
Abstract
Obesity is a risk factor for the development of type 2 diabetes and cardiovascular disease. However, it is now recognized that a subset of individuals have reduced cardiometabolic risk despite being obese. Paradoxically, a subset of lean individuals is reported to have high risk for cardiometabolic complications. These distinct subgroups of individuals are referred to as metabolically unhealthy normal weight (MUNW) and metabolically healthy obese (MHO). Although the clinical relevance of these subgroups remains debated, evidence shows a critical role for white adipose tissue (WAT) function in the development of these phenotypes. The goal of this review is to provide an overview of our current state of knowledge regarding the molecular and metabolic characteristics of WAT associated with MUNW and MHO. In particular, we discuss the link between different WAT depots, immune cell infiltration, and adipokine production with MUNW and MHO. Furthermore, we also highlight recent molecular insights made with genomic technologies showing that processes such as oxidative phosphorylation, branched-chain amino acid catabolism, and fatty acid β-oxidation differ between these phenotypes. This review provides evidence that WAT function is closely linked with cardiometabolic risk independent of obesity and thus contributes to the development of MUNW and MHO.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".