Gene–Environment Interaction and the Metabolic Syndrome
Bibliographic record
Abstract
The metabolic syndrome, which has been shown to affect as many as 20% of the general adult US population, is generally described as a cluster of cardiovascular risks factors, most notably obesity, type 2 diabetes or resistance to insulin-stimulated glucose uptake (insulin resistance), dyslipidaemia and hypertension. All these risk factors are under both genetic and environmental control; they are considered individually as complex genetic diseases. Prior to pharmacological interventions for hypertension, diabetes and dyslipidaemia, lifestyle changes, in particular weight loss (or weight maintenance) and physical activity, were prioritized and constituted an effective first-line intervention strategy. Here we want to focus on three clinical components of the metabolic syndrome and the environmental factors that are considered to be the most significant targets for primary interventions: type 2 diabetes and exercise, obesity and diet, and hypertension and salt. Our experimental approach is to go from candidate gene strategy to genome-wide association. The identification of the genetic component of these risk factors is a major challenge, and it is hoped that this would help unravel mechanistic pathways that can ultimately serve as new targets for therapeutic intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".