High-Density Lipoprotein Cholesterol and Apolipoprotein A1 in Synovial Fluid: Potential Predictors of Disease Severity of Primary Knee Osteoarthritis
Bibliographic record
Abstract
Objectives The aim of this study was to detect levels of common lipid species in serum and synovial fluid (SF) of primary knee osteoarthritis (OA) patients and investigate their correlations with disease severity. Materials and Methods The study enrolled 184 OA patients receiving arthroscopic debridement or total knee arthroplasty and 180 healthy controls between April 2012 and March 2018. Total triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB) levels were analyzed in serum and SF of OA patients, and in serum of healthy individuals. The Noyes rating criteria, Kellgren-Lawrence (KL) grading system, and Western Ontario McMaster University Osteoarthritis Index (WOMAC) scores were, respectively, used to assess cartilage damage, radiographic severity, and symptomatic severity of OA. Results No significant differences were found in serum TG and ApoB levels between the 2 groups, while OA patients had higher TC and LDL-C levels and lower HDL-C and ApoA1 levels ( P < 0.05). Pearson correlation analysis revealed SF HDL-C and ApoA1 levels were negatively correlated with cartilage damage scores, KL grades as well as WOMAC scores ( P < 0.05), which were still significant after adjusting for confounding factors ( P < 0.05). Receiver operating characteristic curve analysis revealed SF HDL-C (area under the curve [AUC]: 0.816) and ApoA1 (AUC: 0.793) were also good predictors of advanced-stage OA ( P < 0.001). Conclusion SF HDL-C and ApoA1 levels were negatively correlated with cartilage damage, radiographic severity, and symptomatic severity of primary knee OA, emerging as potential biomarkers for radiographic advanced-stage OA, which may serve as predictors of disease severity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".