Retinol-binding protein 4 is a potential biomarker of changes in lean mass in postmenopausal women
Bibliographic record
Abstract
Identifying biomarkers can help in the early detection of muscle loss and drive the development of new therapies. Research suggests a potential link between retinol-binding protein 4 (RBP4) and muscle mass, particularly in postmenopausal women. This study aimed to examine the association between baseline RBP4 levels and changes in appendicular lean mass (ALM), an indicator of muscle mass, in postmenopausal women. A 12-month follow-up period ( n = 153) included baseline and 12-month ALM assessments using DXA. ALM was normalized to squared height (ALMI). Baseline evaluations encompassed insulin resistance via HOMA-IR and immunoassay magnetic bead panel measurements of RPB4, IL-6, TNF-α, and IL-10. Postmenopausal women were categorized into higher ( n = 77) and lower ( n = 76) RPB4 groups based on baseline RPB4 values. Their changes in ALMI were compared using Mann–Whitney tests. General linear model was employed to evaluate the predictive power of baseline RBP4 for ALMI changes, adjusting for confounding variables: age, physical activity, smoking status, body fat, HOMA-IR, inflammatory markers (TNF-α and IL-6), and anti-inflammatory factor (IL-10). The higher RBP4 group exhibited a more pronounced reduction in ALMI compared to the lower RBP4 group (Higher RBP4 = −0.39 kg/m2, 95% CI: −0.48 to −0.31 kg/m2vs. Lower RBP4 = −0.24 kg/m2, 95% CI: −0.32 to −0.15 kg/m2, P = 0.011). After adjusting for confounding factors, the association between baseline RBP4 changes and ALMI remained ( b = −0.008, SE = 0.002, P < 0.001), indicating higher baseline RBP4 values linked to greater ALMI reduction. Our findings support RBP4 as a potential biomarker for changes in muscle mass in postmenopausal women.
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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 | 0.000 |
| 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".