Racial Differences in Serum Adipokine and Insulin Levels in a Matched Osteoarthritis Sample: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: In an attempt to correlate biomarkers with disease, serum-based biomarkers often are compared between individuals with osteoarthritis (OA) and control subjects. However, variable results have been reported. Some studies have suggested an association between certain adipokines and insulin and OA. We know that there are racial differences in OA prevalence and incidence, and from general population-based studies, those of Asian race consistently demonstrate a unique adipokine/insulin serum concentration profile as compared to Caucasians. Whether similar racial differences exist within OA samples is unknown and may have implications for selecting appropriate controls in comparative studies. METHODS: Serum levels of adipokines, leptin, and adiponectin, along with insulin, were determined by ELISA in patients scheduled for total hip or knee replacement surgery for OA. Fifteen Asian patients were matched 1 : 1 on age (±2 years), gender, body mass index (±1.5 kg/m(2)), and surgical joint with Caucasian patients. Differences in serum concentrations were tested using paired t-tests. RESULTS: Serum leptin and insulin levels were significantly higher in Asians compared to Caucasians (p < 0.05). While serum adiponectin levels were lower among Asians, the difference did not reach statistical significance (p = 0.12). CONCLUSION: Findings from this work suggest that when studying serum biomarker concentrations in OA versus controls, race may be an important factor to consider. Our findings warrant confirmation in larger studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".