Cholesteryl Ester Transfer Protein Influences High-Density Lipoprotein Levels and Survival in Sepsis
Bibliographic record
Abstract
RATIONALE: High-density lipoprotein (HDL) cholesterol (HDL-C) levels decline during sepsis, and lower levels are associated with worse survival. However, the genetic mechanisms underlying changes in HDL-C during sepsis, and whether the relationship with survival is causative, are largely unknown. OBJECTIVES: We hypothesized that variation in genes involved in HDL metabolism would contribute to changes in HDL-C levels and clinical outcomes during sepsis. METHODS: We performed targeted resequencing of HDL-related genes in 200 patients admitted to an emergency department with sepsis (Early Infection cohort). We examined the association of genetic variants with HDL-C levels, 28-day survival, 90-day survival, organ dysfunction, and need for vasopressor or ventilatory support. Candidate variants were further assessed in the VASST (Vasopressin versus Norepinephrine Infusion in Patients with Septic Shock Trial) cohort (n = 632) and St. Paul's Hospital Intensive Care Unit 2 (SPHICU2) cohort (n = 203). MEASUREMENTS AND MAIN RESULTS: We identified a rare missense variant in CETP (cholesteryl ester transfer protein gene; rs1800777-A) that was associated with significant reductions in HDL-C levels during sepsis. Carriers of the A allele (n = 10) had decreased survival, more organ failure, and greater need for organ support compared with noncarriers. We replicated this finding in the VASST and SPHICU2 cohorts, in which carriers of rs1800777-A (n = 35 and n = 12, respectively) had significantly reduced 28-day survival. Mendelian randomization was consistent with genetically reduced HDL levels being a causal factor for decreased sepsis survival. CONCLUSIONS: Our results identify CETP as a critical regulator of HDL levels and clinical outcomes during sepsis. These data point toward a critical role for HDL in sepsis.
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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 | 0.000 |
| 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".