CETP genetic variant rs1800777 (allele A) is associated with abnormally low HDL-C levels and increased risk of AKI during sepsis
Bibliographic record
Abstract
High-density cholesterol (HDL-C) levels are influenced by genetic variation in several genes. Low levels of HDL-C have been associated with increased risk of acute kidney injury (AKI). We investigated whether genetic polymorphisms in ten genes known to regulate HDL-C levels are associated with both HDL-C levels and AKI development during sepsis. Two cohorts were retrospectively analyzed: Derivation Cohort (202 patients with sepsis enrolled at the Emergency Department from 2011 to 2014 in Vancouver, Canada); Validation Cohort (604 septic shock patients enrolled into the Vasopressin in Septic Shock Trial (VASST)). Associations between HDL-related genetic polymorphisms and both HDL-C levels, and risk for clinically significant sepsis-associated AKI (AKI KDIGO stages 2 and 3) were evaluated. In the Derivation Cohort, one genetic variant in the Cholesteryl Ester Transfer Protein (CETP) gene, rs1800777 (allele A), was strongly associated with lower HDL-C levels (17.4 mg/dL vs. 32.9 mg/dL, P = 0.002), greater CETP mass (3.43 µg/mL vs. 1.32 µg/mL, P = 0.034), and increased risk of clinically significant sepsis-associated AKI (OR: 8.28, p = 0.013). Moreover, the same allele was a predictor of sepsis-associated AKI in the Validation Cohort (OR: 2.38, p = 0.020). Our findings suggest that CETP modulates HDL-C levels in sepsis. CETP genotype may identify patients at high-risk of sepsis-associated AKI.
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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 | 0.002 |
| 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 | 0.001 | 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".