Polymorphisms in<i>PPAR</i>Genes (<i>PPARD</i>,<i>PPARG</i>, and<i>PPARGC1A</i>) and the Risk of Chronic Kidney Disease in Japanese: Cross-Sectional Data from the J-MICC Study
Bibliographic record
Abstract
Chronic kidney disease (CKD) is well known as a strong risk factor for both end stage renal disease and cardiovascular disease. To clarify the association of polymorphisms in the PPAR genes (PPARD, PPARG, and PPARGC1A) with the risk of CKD in Japanese, we examined this association among the Japanese subjects using the cross-sectional data of J-MICC (Japan Multi-Institutional Collaborative Cohort) Study. The subjects for this analysis were 3,285 men and women, aged 35-69 years, selected from J-MICC Study participants; genotyping was conducted by multiplex polymerase chain reaction-based Invader assay. The prevalence of CKD was determined for CKD stages 3-5 (defined as eGFR < 60 ml/min/1.73 m(2)). Participants with CKD accounted for 17.3% of the study population. When those with PPARD T-842C T/T were defined as reference, those with PPARD T-842C T/C and C/C demonstrated the OR for CKD of 1.26 (95%CI 1.04-1.53) and 1.31 (95%CI 0.83-2.06), respectively. There were no significant associations between the polymorphisms in other PPAR genes and the risk of CKD. The present study found a significantly increased risk of CKD in those with the C allele of PPARD T-842C, which may suggest the possibility of personalized risk estimation of this life-limiting disease in the near future.
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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 | 0.003 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".