Plasma apolipoprotein A-I is a causal protective factor in sepsis
Bibliographic record
Abstract
Apolipoprotein AI (ApoAI), the main component of high-density lipoprotein (HDL), binds pathogen lipids to limit inflammation. We performed a retrospective analysis of 442,601 European patients in the UK Biobank (UKB) cohort focused on sepsis patients ( n = 11,643). We tested for a causal contribution of ApoAI using Mendelian randomization with an ApoAI genetic score as an instrumental variable, with sensitivity analyses to control for genetic confounders and instrumental variable assumptions. Sensitivity analyses controlled for confounders, and validation was performed in transancestry sepsis cohorts VASST (Europeans, n = 632) and Chiba (East Asians, n = 536). Median baseline ApoAI levels were lower in individuals who later developed sepsis (1.45 g/L) than in those who did not (1.51 g/L; P < 0.0001). Mendelian randomization in UKB showed ApoAI as protective against sepsis incidence (OR = 0.87, 95%CI [0.86,0.89], P = 7.4 × 10 − 44 ) and 28-day mortality (OR = 0.73, 95%CI [0.71,0.76], P = 8.2 × 10 − 40 ). ApoAI’s protective effect on mortality was validated in VASST (OR = 0.84, 95%CI [0.72,0.99], P = 0.046) and Chiba (OR = 0.69, 95%CI [0.57,0.84], P = 0.0002). Confidence intervals overlapped between all cohorts indicating directional agreement of causal effect. Multivariable Mendelian randomization was performed to assess independence from other major lipid parameters. In multivariable analysis adjusting for HDL-C, LDL-C, and triglycerides, ApoAI retained a strong independent protective effect (OR = 0.71, 95%CI [0.65, 0.77], P = 2.4 × 10 − 20 ). We further identified a putative mechanism. Specifically, ApoAI causally reduces circulating LPS levels (logOR=-0.23, 95%CI [– 0.22,-0.24], P = 9.1 × 10 − 81 ). We conclude that plasma ApoAI levels provide significant causal protection in sepsis pathobiology possibly by reducing circulating LPS levels.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".