Metabolic Syndrome and Osteoarthritis Distribution in the Hand Joints: A Propensity Score Matching Analysis From the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: To investigate the metabolic syndrome (MetS) association with radiographic and symptomatic hand osteoarthritis (HOA). METHODS: Using 1:2 propensity score matching for relevant confounders, we included 2509 participants (896 MetS positive and 1613 MetS negative) from the Osteoarthritis Initiative dataset. MetS and its components, according to the International Diabetes Federation criteria, were extracted from baseline data, and included hypertension, abdominal obesity, dyslipidemia, and diabetes. We scored distinct hand joints based on the modified Kellgren-Lawrence (mKL) grade of baseline radiographs, with HOA defined as mKL ≥ 2. In the cross-sectional analysis, we investigated the association between MetS and its components with radiographic HOA and the presence of nodal and erosive HOA phenotypes using regression models. In the longitudinal analysis, we performed Cox regression analysis for hand pain incidence in follow-up visits. RESULTS: MetS was associated with higher odds of radiographic HOA, including the number of joints with OA (OR 1.32, 95% CI 1.08-1.62), the sum of joints mKLs (OR 2.42, 95% CI 1.24-4.71), mainly in distal interphalangeal joints (DIPs) and proximal interphalangeal joints (PIPs; OR 1.52, 95% CI 1.08-2.14 and OR 1.38, 95% CI 1.09-1.75, respectively), but not metacarpophalangeal (MCP) and first carpometacarpal (CMC1) joints. Hand pain incidence during follow-up was higher with MetS presence (HR 1.25, 95% CI 1.07-1.47). The erosive HOA phenotype and joints' nodal involvement were more frequent with MetS (OR 1.40, 95% CI 1.01-1.97 and OR 1.28, 95% CI 1.02-1.60, respectively). CONCLUSION: MetS, a potentially modifiable risk factor, is associated with radiographic DIP and PIP OA and longitudinal hand pain incidence while sparing MCPs and CMC1s. Nodal and erosive HOA phenotypes are associated with MetS, suggestive of possible distinct pathophysiology.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".