Measurement of bioactive osteocalcin in humans using a novel immunoassay reveals association with glucose metabolism and β-cell function
Bibliographic record
Abstract
Osteocalcin (OCN) is a bone-derived hormone involved in the regulation of glucose metabolism. In serum, OCN exists in carboxylated and uncarboxylated forms (ucOCN), and studies in rodents suggest that ucOCN is the bioactive form of this hormone. Whether this is also the case in humans is unclear, because a reliable assay to measure ucOCN is not available. Here, we established and validated a new immunoassay (ELISA) measuring human ucOCN and used it to determine the level of bioactive OCN in two cohorts of overweight or obese subjects, with or without type 2 diabetes (T2D). The ELISA could specifically detect ucOCN concentrations ranging from 0.037 to 1.8 ng/mL. In a first cohort of overweight or obese postmenopausal women without diabetes ( n = 132), ucOCN correlated negatively with fasting glucose (r = −0.18, P = 0.042) and insulin resistance assessed by the homeostatic model assessment of insulin resistance (r = −0.18, P = 0.038) and positively with insulin sensitivity assessed by a hyperinsulinemic-euglycemic clamp (r = 0.18, P = 0.043) or insulin sensitivity index derived from an oral glucose tolerance test (r = 0.26, P = 0.003). In a second cohort of subjects with severe obesity ( n = 16), ucOCN was found to be lower in subjects with T2D compared with those without T2D (2.76 ± 0.38 versus 4.52 ± 0.06 ng/mL, P = 0.009) and to negatively correlate with fasting glucose (r = −0.50, P = 0.046) and glycated hemoglobin (r = −0.57, P = 0.021). Moreover, the subjects with ucOCN levels below 3 ng/mL had a reduced insulin secretion rate during a hyperglycemic clamp ( P = 0.03). In conclusion, ucOCN measured with this novel and specific assay is inversely associated with insulin resistance and β-cell dysfunction in humans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".