Effect of RANKL on Lower Depressive Symptoms In Hemodialysis Patients
Bibliographic record
Abstract
Depression and osteoporosis are common diseases in dialysis patients. In addition, patients with osteoporosis are more susceptible to depression. Contrary to previous anti-osteoporosis agents, denosumab and romosozumab could be used in dialysis patients and have similar action mechanisms for blocking RANKL. RANKL causes bone resorption after binding RANKL, but binding with OPG leads to suppress of bone resorption. In recent mice study, inhibition of RANKL with denosumab improved depressive-like phenotype. Besides, it was found that OPG was associated with depression. Therefore, this study aimed to investigate the association of depressive symptoms with RANKL and OPG in hemodialysis patients. We conducted a cross-sectional study with a total of 172 hemodialysis patients. The participants were measured for plasma RANKL, OPG, MMP-2, and MMP-9 levels. Logistic regression analysis was performed to evaluate the effect of RANKL and OPG on the presence of depressive symptoms. The depressive symptoms were observed in 90 (52.3%) subjects. RANKL tertile 3 had negative association with BDI score (β - 4.527, 95% CI - 8.310 to - 0.743) in univariate analysis, and this association persisted even after multivariate adjustments (β - 5.603, 95% CI - 9.715 to -1.491) in linear regression. In logistic regression between RANKL tertiles and depressive symptoms, RANKL tertile 3 had significantly lower unadjusted OR (0.40, 95% CI 0.19-0.86), and multivariate-adjusted OR (0.31, 95% CI 0.12-0.82) for depressive symptoms. OPG was not significantly associated with depressive symptoms. Higher plasma RANKL concentrations were significantly associated with lower depressive symptoms in HD patients.Trial registration WHO registry, No. KCT0003281, date: January 12, 2017.
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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 | 0.000 | 0.000 |
| 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".