Mapping body fat distribution: A key step towards the identification of the vulnerable patient?
Bibliographic record
Abstract
Although excess body fat is a significant health hazard, estimation of body fat content with the body mass index may not adequately reflect the amount of atherogenic adipose tissue (AT), i.e. visceral and ectopic fat. As opposed to subcutaneous AT that supposedly acts as a metabolic sink buffering excess dietary energy, visceral or intra-abdominal AT depots respond to several external stimuli that trigger lipolysis and secretion of free fatty acids (FFAs). Reaching the liver, FFAs accumulate in the liver and, over time, promote a chronic condition known as non-alcoholic fatty liver disease (NAFLD). The liver of the typical NAFLD patient secretes large amounts of very-low-density lipoproteins, the lipid content of which may accumulate in additional organs (skeletal muscle, heart, and pancreas). Here, we review the evidence emerging from functional and population studies that point towards an important role of ectopic fat accumulation in the pathophysiology of type 2 diabetes and cardiovascular disease. We conclude that although patients with impaired glycemic control or type 2 diabetes are at increased cardiovascular disease (CVD) risk, estimating cardiovascular risk goes wellbeyond the assessment of glycemic control and traditional CVD risk factors, and the estimation of visceral/ectopic fat deposition via readily available imaging techniquesshould be considered.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".