Serum level of fetuin B is associated with osteoporosis: a 4-year prospective study in China
Bibliographic record
Abstract
PURPOSE: As a novel hepatokine, fetuin B involves in various functions of energy metabolism. Recent advance reveals a complex interaction between bone and liver via the secretion of hepatokines. The association between serum fetuin B and osteoporosis was evaluated in a 4-year hospital-based prospective study of 1,370 Chinese postmenopausal women. METHODS: Bone mineral densities (BMDs) were obtained on femoral neck and lumbar spines by dual energy X-ray absorptiometry. Serum fetuin B level was tested by enzyme-linked immunosorbent assay. RESULTS: Of the 1,370 participants in the baseline study (2012), 650 subjects were included in the 4-year follow-up study (2016). Serum fetuin B level presented higher in subjects with osteoporosis (106.7 ± 17.6 μg/ml) than it in controls (86.3 ± 17.5 μg/ml) (P < 0.001). Meanwhile, fetuin B positively correlated with triglycerides (r = 0.227, P = 0.001), femoral BMD (r = -0.426, P < 0.001) and lumbar BMD (r = -0.332, P < 0.001). At the 4-year follow-up, 116 subjects had developed osteoporosis. Serum fetuin B concentration was significantly higher in subjects who developed (P < 0.001). The osteoporosis incidence increased from Q1 9.9%, Q2 14.7%, and Q3 17.8% to Q4 30.2% (P for trend < 0.001), among the quartiles of baseline fetuin B. A higher fetuin B baseline level was linked to the incidence of osteoporosis (adjusted OR = 1.179, 95% CI [1.119 - 1.243], P = 0.009). CONCLUSION: Serum fetuin B levels increased with the development of osteoporosis.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".