A Single Nucleotide Polymorphism in NF-κB Inducing Kinase Is Associated with Mortality in Septic Shock
Bibliographic record
Abstract
We tested the hypothesis that single nucleotide polymorphisms (SNPs) within genes of the NF-κB pathway are associated with altered clinical outcome of septic shock patients. We genotyped 59 SNPs in the NF-κB pathway in a discovery cohort of septic shock patients (St. Paul's Hospital [SPH], N = 589), which identified the C allele of rs7222094 T/C within MAP3K14 (NF-κB inducing kinase; NIK) associated with increased 28-d mortality (uncorrected p = 0.00024, Bonferroni corrected p = 0.014). This result was replicated in a second cohort of septic shock patients (Vasopressin and Septic Shock Trial [VASST; N = 616]) in which the CC genotype of rs7222094 was associated with increased 28-d mortality (Cox regression: SPH cohort hazard ratio [HR], 1.35; 95% confidence interval [CI], 1.12-1.64; p = 0.002 Caucasian only; and VASST cohort HR, 1.24; 95% CI, 1.00-1.52; p = 0.048 Caucasian only). Patients having the CC genotype of rs7222094 in SPH experienced more renal and hematological dysfunction (p = 0.003 and p = 0.011), while patients of the VASST cohort with the rs7222094 CC genotype showed the same trend toward more renal dysfunction. In lymphoblastoid cell lines, we found the rs7222094 genotype most strongly associated with mRNA expression of CXCL10, a chemokine regulated by NF-κB. Accordingly, we measured CXCL10 protein levels and found that the CC genotype of rs7222094 was associated with significantly lower levels than those of the TT genotype in lymphoblastoid cell lines (p < 0.05) and in septic shock patients (p = 0.017). This suggests that the CC genotype of NIK rs7222094 is associated with increased mortality and organ dysfunction in septic shock patients, perhaps due to altered regulation of NF-κB pathway genes, including CXCL10.
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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 | 0.002 |
| 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 | 0.001 |
| 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".