Genetic predisposition to depression and inflammation impacts symptom burden and survival in patients with head and neck cancer: A longitudinal study
Bibliographic record
Abstract
OBJECTIVE: The primary purpose of this study was to investigate the contribution of genetic predispositions to depression and inflammation, as measured through polygenic risk scores, on symptom burden (physical and psychological) in patients with head and neck cancer in the immediate post-treatment period (i.e., at three months post-diagnosis), as well as on 3-, 6-, 12-, 24- and 36-month survival. METHODS: Prospective longitudinal study of 223 adults (72 % participation) newly diagnosed with a first occurrence of primary head and neck cancer, paired with genetic data (Illumina PsychArray), validated psychometric measures, Structured Clinical Interviews for DSM Disorders (SCID-I), and medical chart reviews. RESULTS: adj. = 0.38, p < 0.001): a baseline SCID-I Anxiety Disorder (b = 1.69, B = 0.23, 95%CI = 0.43-2.94; p = 0.009), baseline levels of HADS anxiety (b = 0.20, B = 0.29, 95%CI = 0.07-0.34; p = 0.003), the polygenic risk score (PRS) for depression (b = 0.66, B = 0.18, 95%CI = 0.003-1.32; p = 0.049), and cumulated dose of radiotherapy (b = 0.002, B = 0.46, 95%CI = 0.001-0.003; p < 0.001). When controlling for factors known to be associated with cancer survival, patients with a higher PRS associated with depression and inflammation, respectively, presented higher risk of death within 36 months (b = 1.75, Exp(B) = 5.75, 95%CI = 1.55-21.27, p = 0.009 and b = 0.14, Exp(B) = 1.15, 95%CI = 1.01-1.30, p = 0.03). CONCLUSIONS: Our results outline three potential pathways of symptom burden in patients with head and neck cancer: a genetic predisposition towards depression; an initial anxiety disorder upon being diagnosed with cancer or high levels of anxiety upon diagnosis; and a dose-related response to radiotherapy. One may want to investigate early interventions in these areas to alleviate symptom burden in patients faced with a life-threatening disease, as well as consider targeting genetic predisposition towards depression and inflammation implicated in survival. The high prevalence of distress in patients with head and neck cancer is an opportunity to study genetic predispositions, which could potentially be broadly generalized to other cancers and diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".