Peroxisome proliferator-activated receptor gamma coactivator 1α variation: a closer look at obesity onset age and its related metabolic status and body composition
Bibliographic record
Abstract
There is a lack of knowledge regarding the effect of polymorphisms of the peroxisome proliferator-activated receptor gamma coactivator 1α (PGC-1α) gene on the age of obesity onset. Hence, 3 polymorphisms of the PGC-1α gene (PGC-1α rs17574213, rs8192678, and rs3755863) were examined in association with obesity onset age. Also, obesity onset age-related metabolic status and body composition were evaluated. This cross-sectional study was conducted with a total of 321 obese participants. Anthropometric and biochemical information, body composition, and PGC-1α gene sequences were analyzed. The rs17574213 polymorphism was associated with obesity onset in children aged <1 years and 10-18 years. The rs8192678 polymorphism was associated with obesity onset in adulthood. Body mass index, body fat percent, and trunk fat were higher in groups whose obesity began at age <1 year or 10-18 years than in other groups. Serum levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and total cholesterol were lowest in the group with obesity onset between the ages of 10 and 18 years. Visceral fat and fasting blood glucose were highest in those whose obesity began in adulthood. In conclusion, 2 polymorphisms of the PGC-1α gene were associated with obesity onset age.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".