Prediction of susceptible biomarkers for end stage renal disease among North Indians
Bibliographic record
Abstract
AIM: Involvement of pro-inflammatory genes has been correlated with basic kidney diseases and end stage renal disease (ESRD). However, results at odds were often noted from such independent association studies. This study proposes a genome wide analysis approach to predict ESRD risk associated genes. METHODS: We included 42 single nucleotide polymorphisms (SNPs) showing association among north Indian ESRD cases and controls. ESRD cases comprised chronic glomerulonephritis (CGN), chronic interstitial nephritis (CIN), hypertension (HTN) and autosomal dominant polycystic kidney disease (ADPKD). Genotyping data obtained from our prior published reports were compared with Genome-Wide Association Studies (GWAS) SNPs retrieved from HapMap and GWASCentral databases using R-statistical package SNPAssoc. Linkage disequilibrium (LD), gene-gene interaction, classification and regression tree (CART) and pathway analysis were carried out in the present study supplemented with IL-6 and TNF-α levels estimation using enzyme linked immunosorbent assay (ELISA). RESULTS: Comparison of genotyping data with GWAS SNPs revealed significant associations for interleukin (IL)1-RN, IL-6, MTHFR, tumour necrosis factor-α (TNF-α) and CCR3 genes with ESRD. Nine SNPs were commonly associated with CGN, CIN, HTN, ADPKD and ESRD. LD (D = 0.9) and gene-gene interaction (P = 0.0002) analyses revealed significant associations for IL-6 and TNF-α genes. In a consistent manner, CART analysis and functional analysis servers predict predisposing effects for TNF-α and IL-6 with ESRD. Finally, higher body circulating levels were observed for mutant TNF-α and IL-6 alleles among ESRD. CONCLUSION: The study indicates significance for IL-6 and TNF-α gene with basic kidney diseases and ESRD. Extensive statistical tests, pathway analysis and functional assays also reflect attenuated level of significance for these SNPs. In future these may be brought from bench side to clinical practice as diagnostic biomarkers upon external and prospective replication and confirmation among other cohorts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".