Evidence for Interaction between<i>PPARG</i>Pro12Ala and<i>PPARGC1A</i>Gly482Ser Polymorphisms in Determining Type 2 Diabetes Intermediate Phenotypes in Overweight Subjects
Bibliographic record
Abstract
BACKGROUND: The peroxisome proliferator-activated receptor-gamma ( PPARG) Pro12Ala and the PPARG co-activator-1alpha ( PPARGC1A) Gly482Ser polymorphisms (SNPs) have been associated with type 2 diabetes mellitus (T2DM) risk. We hypothesized that independent and interactive effects of the PPARG Pro12Ala and PPARGC1A Gly482Ser polymorphisms influence T2DM intermediate phenotypes. MATERIAL AND METHODS: PPARG Pro12Ala and PPARGC1A Gly482Ser SNPs were studied in 680 non diabetic subjects who underwent a 75 g oral glucose tolerant test (OGTT). Glucose and insulin plasma levels in the fasting state and derived from the OGTT were included in the present study. RESULTS: We found significant independent effects of the PPARG and PPARGC1A variants on fasting insulin levels (p=0.02 for both), HOMA-IR (p=0.03 and p=0.02, respectively), insulin area under the curve (AUC) (p=0.007 and p=0.006, respectively) and 2-h glucose levels (p=0.02 for PPARGC1A). Furthermore, significant gene-gene interactions were found for fasting insulin, HOMA-IR and insulin AUC (p=0.03 for all). Carriers of the PPARGC1A Gly allele who were also PPARG Ala-carriers had higher fasting insulin levels (p=0.02), HOMA-IR (p=0.01) and insulin AUC (p=0.01) compared to the Ser/Ser-Ala+genotype combination, whereas no differences between the PPARGC1A genotypes among the PPARG Pro/Pro carriers were observed. CONCLUSION: Together, these results showed that PPARG Pro12Ala and PPARGC1A Gly482Ser variants are associated, alone and in interaction, with insulin and glucose homeostasis and suggest that gene-gene interactions should be taken into account in candidate gene studies of T2DM to identify subjects with markedly different risks of developing the disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".