Pro‐ and anti‐inflammatory cytokine associations with major depression in cancer patients
Bibliographic record
Abstract
OBJECTIVE: Cytokines may be linked to depression, although it has been challenging to demonstrate this association in cancer because of the overlap between depressive symptoms and other sickness behaviors. This study investigates the relationship between cytokines and depression in cancer patients, accounting for confounding clinical and methodological factors. METHODS: The GRID Hamilton Rating Scale for Depression and Neurotoxicity Rating Scale (NRS) for cytokine-induced sickness behaviors were administered to 61 cancer patients and 38 healthy controls. The cancer group was of mixed type and largely of late stage, with a recruitment rate of 35% and completion rate of 47%. Major depression was diagnosed in 19 of 61 (31%) cancer patients. Multiplexed cytokine assays for inflammatory and anti-inflammatory cytokines were conducted in plasma samples using electrochemiluminescence. RESULTS: All cancer patients had high NRS scores and elevated levels of most cytokines. Cancer patients with major depression had higher NRS scores than those without major depression. IL-1rα was positively associated with the GRID scores of depressive symptoms (regression coefficient, 3.52 ± 1.18; P = .004), but not with major depression. Major depression was negatively associated with the anti-inflammatory cytokine IL-4 (regression coefficient, -0.65 ± 0.26; P = .013), but not with IL-1rα. CONCLUSIONS: Depressive symptoms in cancer patients may represent sickness behaviors, which may have distinct cytokine associations from major depression. Sickness behaviors may be associated with an increase in inflammatory cytokines, whereas major depression may be induced by a failure to adequately resolve inflammation. Our findings suggest that cytokine-mediated interventions may be of value to treat depression in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".