Association of Serum <scp>Low‐Density</scp> Lipoprotein, <scp>High‐Density</scp> Lipoprotein, and Total Cholesterol With Development of Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Studies suggest an association between elevated total serum cholesterol, particularly low-density lipoprotein (LDL), and osteoarthritis (OA). The present study was undertaken to evaluate the association between total cholesterol, LDL, and high-density lipoprotein (HDL) and risk of knee OA. METHODS: We studied participants from the Multicenter Osteoarthritis study (MOST) cohort at risk of developing knee OA. From baseline through 7 years, repeated knee radiographs and magnetic resonance images (MRIs) were obtained, and knee symptoms were queried. From baseline fasting blood samples, lipids and lipoproteins were analyzed using standard assays. After excluding participants with baseline OA, we defined 2 sets of patients: those developing radiographic OA, and those developing symptomatic OA (knee pain and radiographic OA). Controls did not develop these outcomes. Additionally, we examined worsening of cartilage loss and synovitis on MRI and of knee pain using the Western Ontario and McMaster Universities Osteoarthritis Index scale. We carried out logistic regression adjusting for age, sex, body mass index, education, baseline pain, and depressive symptoms, testing total cholesterol and lipoproteins as continuous measures, and we performed sensitivity analyses examining whether commonly used thresholds for high cholesterol, LDL, or low HDL increased risk. RESULTS: We studied 337 patients with incident symptomatic OA and 283 patients with incident radiographic OA. The mean age at baseline was 62 years (55% women). Neither total cholesterol, LDL, nor HDL showed a significant association with radiographic or symptomatic OA. Additionally, we found no association of these lipid measures with cartilage loss, worsening synovitis, or worsening knee pain. CONCLUSION: Our data do not support an association between total cholesterol, LDL, or HDL with OA outcomes.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".