Bibliographic record
Abstract
Obesity is a highly prevalent chronic disease and major driver of both atherosclerotic heart disease and heart failure. Obesity is also a heritable neuronal disease with heritability estimates of up to 70%. In this work I review how common genetic variants, usually with small effect sizes, contribute to the risk for developing obesity and cardiometabolic disease in the majority of people and how this can be further modulated by environmental factors. In some individuals, rare genetic variants with large effect sizes can influence the risk of developing obesity, in some cases in a Mendelian manner. I also address how identification of these rare variants has led to fundamental biologic insights into how satiety and reward are biologic processes, has led to more personalized treatments, and has identified potential novel drug treatments. Biologic insights derived from genetic studies of obesity have also improved our understanding of the causal mediators between obesity and cardiovascular disease. A major limitation of studies to date is that they involved mostly people of European ancestry. Studying more diverse populations will improve our understanding of obesity and cardiometabolic disease. Lessons derived from genetic studies make a compelling case for increasing accessibility to therapies that have sustained efficacy in managing obesity and improving health. This increased knowledge must also inform public health initiatives that will reduce the prevalence of obesity in the coming decades.
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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 | 0.004 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".