Abstract We0048: Postoperative Inflammation Impairs Reverse Cholesterol Transport and Fuels Postoperative Plaque Necrosis
Bibliographic record
Abstract
Background: Over 10 million patients undergoing non-cardiac surgery annually experience major cardiovascular complications within 30 days. Atherosclerosis is implicated in a significant number of cases, yet how plaque disruption is precipitated in the perioperative period remains unclear. Reverse cholesterol transport (RCT), which facilitates cholesterol removal via HDL and apoA-I, plays a key role in preventing plaque progression. Surgery-associated inflammation can impair HDL function, but its effects on RCT and plaque stability remain poorly understood. Hypothesis: Inflammatory remodeling of the HDL proteome impairs reverse cholesterol transport, promoting cholesterol accumulation and necrotic core expansion in atherosclerotic plaques. Results: ApoE -/- mice fed a Western diet underwent exploratory abdominal surgery or anesthesia only (control). RCT was impaired for at least 48 hours postoperatively, paralleling reduced in vitro cholesterol efflux to plasma and isolated HDL. Proteomic analysis via LC-MS/MS revealed extensive postoperative inflammatory remodeling of the HDL proteome, with a marked increase in SAA protein abundance and a concurrent reduction in ApoA-I. This acute-phase HDL remodelling amounted to a reduction of several cholesterol transport-related biological pathways as defined by Gene Ontology (GO). Atherosclerotic plaques exhibited a 1.6-fold increase in intracellular lipids (BODIPY) and perilipin-2 at 24 hours post-surgery, with elevated cleaved-caspase-3 expression in lipid-rich cells. By day 15, lipid-rich cells fell below control levels, while plaque necrotic core area expanded by 1.5-fold, suggesting that acute lipid accumulation triggers cell death and necrotic core growth. Preliminary experiments intervening with rApoA-I postoperatively partially restored RCT, lessened acute cholesterol accumulation in plaques, and modulated postoperative inflammation, with ongoing studies assessing its ability to prevent necrotic core expansion. Conclusion: Postoperative inflammation profoundly impairs RCT, driving acute lipid accumulation, cell death, and plaque destabilization. These findings highlight rapid lipid dynamics in plaques during surgical inflammation and underscore the need for targeted strategies to mitigate postoperative atherogenic mechanisms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.012 | 0.002 |
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".