Obesity and cardiovascular disease: friend or foe?
Bibliographic record
Abstract
Obesity is currently one of the greatest public health issues worldwide. However, despite its known deleterious effects on the cardiovascular system and its association with numerous cardiovascular diseases (CVD), recent findings leading to the development of concepts such as metabolically healthy obesity, the obesity paradox, and protective subcutaneous fat depots have raised a lively debate on the disparate effects of obesity on health outcomes. Regarding the concept of metabolically healthy obesity, by presumably labelling a subset of obese people as metabolically healthy, physicians may not feel pressed to curb the current obesity epidemic and prevent the next generation of people from becoming obese. Another issue is that the most commonly used anthropometric index to define obesity, the body mass index, is at the core of the controversy because of its limitations including its inability to discriminate between fat mass and muscle mass. Many recent epidemiological and metabolic studies have used other indices such as waist-hip ratio, waist circumference, and imaging (computed tomography or magnetic resonance imaging) measurements of visceral adiposity and of ectopic fat depots. In addition, emerging evidence supports the importance of cardiorespiratory fitness, skeletal muscle mass and strength in patients with obesity as useful variables to predict CVD risk beyond adiposity. In this review, we will discuss the complex and disparate effects of obesity on CVD, particularly focusing on whether, under given circumstances, it could be harmful, potentially harmless or neutral, or even possibly protective.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".