Uncarboxylated Osteocalcin Levels in Patients With Metabolic Syndrome and Their Association With Metabolic Parameters
Bibliographic record
Abstract
Background: Uncarboxylated osteocalcin (uOCN) synthesized by osteoblasts was shown to increase insulin secretion and peripheral insulin sensitivity in pancreatic islets. The present study evaluated the uOCN levels and its association with metabolic parameters in non-diabetic patients with metabolic syndrome (MetS) to test our hypothesis that uOCN levels may be lower in MetS, which is characterized by insulin resistance. Methods: The study included 30 patients with MetS aged 18 years and above (13 male, 17 female, mean age 39.00 ± 5.09 years) and 30 healthy controls (15 male, 15 female, mean age 36.23 ± 6.71 years). Diabetics and post-menopausal women were excluded. The International Diabetes Federation criteria were used to define MetS. Groups were compared depending on their uOCN levels, and association of uOCN with metabolic parameters was assessed. Results: Serum u0CN levels were 5.56 ± 3.36 ng / mL in patients with MetS whereas they were 6.26 ± 2.65 ng / mL in healthy controls (P = 0.183). Serum uOCN levels showed a negative correlation with HbA1c and body mass index (r = -0.308, P = 0.017; r = -0.278, P = 0.032, respectively) in all patients, and with waist circumference (r = -0.190, P = 0.032; r = -0.379, P: 0.047, respectively) in males. Conclusion: Although the difference in uOCN levels between patients with MetS and control group was not statistically significant, a negative correlation of uOCN with HbA1c, body mass index and waist circumference may provide support to our hypothesis that uOCN levels may be lower in MetS. doi:10.4021/jem87w
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".