Matrix Metalloproteinase-9 Gene Polymorphisms and Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to explore the associations between the prevalence of chronic kidney disease (CKD) and polymorphisms in the genes encoding matrix metalloproteinases (MMPs) and tissue inhibitor of matrix metalloproteinases (TIMPs). MMPs degrade extracellular matrix proteins in the glomerulus, and play important roles in kidney disease progression. METHODS: DNA samples from 3,309 subjects aged 35-69 years were genotyped for 10 potentially functional polymorphisms in MMP and TIMP genes. The prevalence of CKD (estimated glomerular filtration rate <60 ml/min/1.73 m(2)) was compared among the genotypes. RESULTS: The prevalence of CKD decreased significantly with the number of minor alleles in MMP9 C-1562T (odds ratios (ORs) 0.77 for CT and 0.65 for TT compared with CC; p for trend = 0.023) and MMP9 R668Q (ORs, 0.79 for RQ and 0.64 for QQ compared with RR; p for trend = 0.024). The haplotype MMP9 -1562T/279R/668Q showed a reduced risk for CKD compared with the most common -1562C/279R/668R (OR 0.77, p = 0.008), and the genotype combination -1562TT/ 279RR/668QQ showed a halved risk for CKD compared with major allele homozygous -1562CC/279RR/668RR (OR 0.53, p = 0.091). CONCLUSION: The potentially functional polymorphisms of MMP9 were associated with the prevalence of CKD in a large Japanese population. These genotypes have been reported to increase MMP9 expression, supporting the hypothesis that MMP-9 has a protective role in the progression of kidney diseases.
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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) | 0.002 | 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".