Linking Metabolic Disease With the PGC-1α Gly482Ser Polymorphism
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor γ coactivator 1-α (PGC-1α) is a highly conserved transcriptional coactivator enriched in metabolically active tissues including liver, adipose, pancreas, and muscle. It plays a role in regulating whole body energy metabolism and its deregulation has been implicated in type 2 diabetes (T2D). A single nucleotide variant of the PPARGC1A gene (rs8192678) is associated with T2D susceptibility, relative risk of obesity and insulin resistance, and lower indices of β cell function. This common polymorphism is within a highly conserved region of the bioactive protein and leads to a single amino acid substitution (glycine 482 to serine). Its prevalence and effects on metabolic parameters appear to vary depending on factors including ethnicity and sex, suggesting important interactions between genetics and cultural/environmental factors and associated disease risk. Interestingly, carriers of the serine allele respond better to some T2D interventions, illustrating the importance of understanding functional impacts of genetic variance on PGC-1α when targeting this pathway for personalized medicine. This review summarizes a growing body of literature surrounding possible links between the PGC-1α Gly482Ser single nucleotide polymorphism and diabetes, with focus on key clinical findings, affected metabolic systems, potential molecular mechanisms, and the influence of geographical or ethnic background on associated risk.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".