Uncloaking the Genetic Determinants of Metabolic Syndrome
Bibliographic record
Abstract
The metabolic syndrome (MetS) is a commonly encountered cluster of clinical phenotypes, including central obesity, hypertension, hyperglycemia, and dyslipidemia. Identifying genetic determinants of MetS will lead to better understanding of its progression and pathogenesis. To further the knowledge of MetS it is important to not only study the candidate genes for each individual component but to also investigate patients with rare monogenic disorders who express a cluster of the phenotypes commonly observed in MetS, however defined. In addition, certain genetic variants have been observed to increase or decrease the risk of developing the entire syndrome. It is apparent that only through complete understanding of the gene-gene, gene-gender and gene-nutrition interactions underlying MetS, will it become possible to determine or minimize the principal complications, namely type 2 diabetes and cardiovascular disease. In this review, we focus on the current evidence for common gene polymorphisms that predispose to or protect from the development of MetS.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".