Polymorphisms in genes encoding antioxidant enzymes (SOD2, CAT, GPx, TXNRD, SEPP1, SEP15 and SELS) and 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 of end-stage renal disease and cardiovascular disease. To clarify the association of polymorphisms in the genes encoding antioxidant enzymes (SOD2, CAT, GPx, TXNRD, SEPP1, SEP15 and SELS) with the risk of CKD in Japanese, we examined this association using the cross-sectional data of Japan Multi-Institutional Collaborative Cohort (J-MICC) Study. The subjects were 3,285 men and women, aged 35-69 years, selected from J-MICC Study participants for whom genotyping were conducted by multiplex polymerase chain reaction-based Invader assay. The prevalence of CKD was determined for CKD stages 3-5 (eGFR <60 ml/min/1.73 m(2)). When those with CAT C-262T C/C were defined as reference, those with CAT C-262T C/T demonstrated the OR for CKD of 0.67 (95% CI 0.43-1.06) with the marginally significant trend for decreased odds ratio with increasing numbers of T allele (p = 0.070). There were no significant associations between the other polymorphisms with CKD risk. The present study found a marginally significant trend of the decreased risk of CKD with increasing numbers of T allele of CAT, 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 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) | 0.000 | 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".