Low cholesterol along with inflammation predicts morbidity and mortality in hemodialysis patients
Bibliographic record
Abstract
Low and not high cholesterol seems to predict high mortality in hemodialysis (HD) patients. The confirmation of this reverse epidemiology as well as its possible interconnection with the increased inflammatory activity observed in this population is being explored in the present study. A group of 136 HD patients was prospectively studied for 2 years, and cardiovascular disease (CVD) as well as all-cause mortality and morbidity were recorded. Baseline lipid profile, inflammatory status, and patients' characteristics were studied as potential survival and hospitalization predictors. During the 24-month follow-up, 21 deaths (52.4% due to CVD) and 38 hospitalizations (55.3% due to CVD) were recorded. In multivariate Cox regression analysis, decreased interleukin-10 (IL-10) and decreased total serum cholesterol (TChol) were the only independent predictors of CVD mortality while C-reactive protein and decreased TChol predicted all-cause mortality. Interleukin-10 at baseline was 11.29 +/- 21.49 vs. 5.51 +/- 4.57 pg/mL (P<0.018) and TChol 167.37 +/- 47.84 vs.122.04 +/- 26.48 mg/dL (P<0.000) in survivors vs. nonsurvivors from CVD, while C-reactive protein at baseline was 9.37 +/- 11.54 vs. 23.15 +/- 18.76 mg/L (P<0.000) and TChol 169.26 +/- 46.42 vs. 133.26 +/- 46.33 mg/dL (P<0.003) in survivors vs. nonsurvivors from any cause of death. Using the same method of statistical analysis, IL-6 and decreased soluble gp130 (sgp130)--an antagonist of IL-6 action--were found to be the only independent prognostic factors for hospitalization due to CVD while decreased soluble gp130 remained the sole predictor of hospitalization due to any cause. In conclusion, reverse epidemiology regarding cholesterol is confirmed in the present study. Furthermore, inflammatory activity also predicts, independently of or in conjunction with low-cholesterol, CVD and all-cause morbidity and mortality in HD patients.
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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 | 0.001 | 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 | 0.001 |
| 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".