Association between depression and inflammatory/anti‐inflammatory cytokines in chronic kidney disease and end‐stage renal disease patients: A review of literature
Bibliographic record
Abstract
Depression is a common psychiatric disorder in patients with advanced chronic kidney diseases (CKDs). Strong correlation has been reported between depression and patients' morbidity and mortality among dialysis patients. On the contrary, chronic inflammation may be a major contributor to morbidity and mortality in these patients. Elevated plasma levels of proinflammatory cytokines, especially C-reactive protein and interleukin (IL)-6, have been correlated with cardiovascular events, hospitalization, and all-cause and cardiovascular-associated mortality in dialysis patients. Studies suggested that inflammation-mediated atherosclerotic cardiovascular diseases are the possible reasons for depression-induced mortality among patients without renal diseases. Several studies found significant elevations in circulating levels of proinflammatory cytokines, particularly IL-6 and tumor necrosis factor-α, in patients with major depression. Furthermore, depressive mood and behaviors, including sadness and suicidal ideation, were observed in patients who received repeated injections of recombinant cytokines. A thorough literature review indicates that while depressive symptoms and elevated inflammatory cytokine levels coexist in CKD and dialysis patients, their association is uncertain. Depression seems to be more associated with elevated serum levels of IL-6 than other cytokines in these patients. Further studies are needed to clarify the possibility of a causal relationship between inflammation and depressive symptoms in CKD and dialysis patients.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".