Obesity Phenotypes, Lifestyle Medicine, and Population Health: Precision Needed Everywhere!
Bibliographic record
Abstract
The worldwide prevalence of obesity is a key factor involved in the epidemic proportions reached by chronic societal diseases. A revolution in the study of obesity has been the development of imaging techniques for the measurement of its regional distribution. These imaging studies have consistently reported that individuals with an excess of visceral adipose tissue (VAT) were those characterized by the highest cardiometabolic risk. Excess VAT has also been found to be accompanied by ectopic fat deposition. It is proposed that subcutaneous versus visceral obesity can be considered as two extremes of a continuum of adiposity phenotypes with cardiometabolic risk ranging from low to high. The heterogeneity of obesity phenotypes represents a clinical challenge to the evaluation of cardiometabolic risk associated with a given body mass index (BMI). Simple tools can be used to better appreciate its heterogeneity. Measuring waist circumference is a relevant step to characterize fat distribution. Another important modulator of cardiometabolic risk is cardiorespiratory fitness. Individuals with a high level of cardiorespiratory fitness are characterized by a lower accumulation of VAT compared to those with poor fitness. Diet quality and level of physical activity are also key behaviors that substantially modulate cardiometabolic risk. It is proposed that it is no longer acceptable to assess the health risk of obesity using the BMI alone. In the context of personalized medicine, precision lifestyle medicine should be applied to the field of obesity, which should rather be referred to as 'obesities.'
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".