Heparin induces an accumulation of atherogenic lipoproteins during hemodialysis in normolipidemic end‐stage renal disease patients
Bibliographic record
Abstract
Dyslipidemias may account for the excess of cardiovascular mortality in end-stage renal disease (ESRD). Lipoprotein studies in ESRD patients are usually relative to prehemodialysis samples even if significative changes may occur after dialysis. In this study, we aimed to investigate the effects of ESRD on triglyceride-rich lipoproteins (TRL) subpopulations distribution and acute change following hemodialytic procedures, including the relative contribution of heparin administration. We selected a group of normolipidemic male middle-aged ESRD patients free of any concomitant disease affecting lipoprotein remnant metabolism compared with controls. We separated TRL subfractions according to density and apoE content and evaluated the changes of these particles after hemodialytic procedures with or without heparin. ESRD subjects had higher TRL subfractions, with the exception of apoE-rich particles, lower high-density lipoprotein (HDL) largest subclasses, and a smaller low-density lipoprotein peak particle size than controls. After a hemodialytic standard procedure with heparin, we demonstrated a significant reduction of triglyceride, an increase of HDL-cholesterol levels, and a raise of small very-low-density lipoprotein, intermediate-density lipoproteins (IDL), apoE-rich particles, and non-HDL-cholesterol levels. When hemodialysis was performed without heparin, no significant changes were observed. In the absence of concomitant hyperlipidemic triggers, ESRD patients show significant lipoprotein abnormalities before dialysis, but without any increased remnant particles concentrations. We speculate that hemodialysis, in particular heparin administration during this procedure, leads to a massive atherogenic TRLs production because of the acute stimulation of the dysfunctional lipolytic system not followed by an efficient removal, determining a recurrent lipoprotein remnant accumulation.
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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 | 0.001 |
| 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".