Serum osteocalcin levels are inversely associated with UACR in Chinese DKD patients: a meta-analysis of 20 clinical studies
Bibliographic record
Abstract
Objective: To systemically assess the relationship between serum osteocalcin levels and the progression of diabetic kidney disease (DKD) in the Chinese population. Methods: The PubMed, Web of Science, CNKI, Wanfang Database, VIP and Chinese Medical Journal full-text Database were searched. Two investigators independently reviewed the literature and extracted data based on predetermined inclusion and exclusion criteria. The Newcastle-Ottawa scale was used to assess the quality of the literature. The statistical analysis was performed using Stata16 software. Results: A total of 20 case-control studies encompassed 4 565 cases, consisting of 643 healthy controls (CN), 1 649 individuals with simple diabetes mellitus (DM), 1 305 with microalbuminuria (MI), and 968 with macroalbuminuria (MA). The meta-analysis results indicated that the serum osteocalcin levels in MI group were significantly lower than those in CN group and DM group [SMD = -1.15, 95% CI (-1.46, -0.85), P < 0.01; and SMD = -0.53, 95% CI (-0.69, -0.37), P < 0.01, respectively], and lower in the MA group compared to the CN group [SMD = -1.28, 95% CI (-1.79, -0.76), P < 0.01]. In the MA group, the serum osteocalcin levels were considerably lower compared to those in DM group and MI group [SMD = -0.93, 95% CI (-1.28, -0.58), P < 0.01; and SMD = -0.41, 95% CI (-0.65, -0.17), P < 0.01, respectively]. Conclusion: The serum osteocalcin levels are typically reduced and show a negative correlation with the severity of proteinuria in Chinese patients with DKD. This indicates a decline in bone formation at early-stage in DKD patients, which worsens as the disease progresses. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/,identifier CRD42024580324.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".