Visceral Obesity
Bibliographic record
Abstract
The worldwide epidemic of obesity, fostered by the modern lifestyle characterized by the lack of physical activity and an energy-dense diet, has contributed to create an unprecedented condition in human history where a majority of overfed individuals will soon surpass the number of malnourished. Obesity-associated disorders, such as diabetes mellitus, an atherogenic dyslipidemia, and hypertension, have undoubtedly contributed to create an atherosclerosis-prone environment and thereby the development of cardiovascular disease (CVD), a leading cause of mortality in Westernized societies. A growing body of evidence indicates that obesity is a heterogeneous condition in which body fat distribution is closely associated with metabolic perturbations and, thus, with CVD risk. In this regard, accumulation of visceral (intra-abdominal) fat is strongly associated with insulin resistance and with a typical atherogenic dyslipidemic state.\n\nThe adipose tissue, once considered a simple energy warehouse, is now regarded as a complex organ not only contributing to the management of energy flux within the body but also interacting with the inflammatory system and the vascular wall. Furthermore, recent studies have underlined that there are intricate interplays among adipocytes, the sympathetic nervous system (SNS), and the renin-angiotensin system (RAS), which participate in the obesity-associated dysmetabolic state. Thus, the adipose tissue is believed to play an important role in the development of both hypertension and other complications related to insulin resistance. However, it should be pointed out that different fat depots have distinct metabolic characteristics, leading to individual differences in the impact of obesity on cardiometabolic risk. Herein, we reviewed the complex links among visceral adiposity, inflammation, and hypertension, along with an attempt to address the clinical implications of these interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.017 |
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".