Genetic Risk For Depression and Quality of Life in Patients With Head and Neck Cancer
Bibliographic record
Abstract
Importance: Although patients with head and neck cancer (HNC) have been shown to experience high distress, few longitudinal studies include a comprehensive evaluation of biopsychosocial factors affecting quality of life (QoL), including genetic risk for depression. Objective: To identify factors at the time of cancer diagnosis associated with QoL scores at 3 months after treatment in patients newly diagnosed with a first occurrence of HNC. Design, Setting, and Participants: This prospective longitudinal study of 1464 participants with a 3-month follow-up, including structured clinical interviews and self-administered measures was carried out at the Department of Otolaryngology Head and Neck Surgery at 2 tertiary care McGill University Affiliated Hospitals, McGill University Health Centre, and Jewish General Hospital. Eligible patients were adults newly diagnosed within 2 weeks with a primary first occurrence of HNC, had a Karnofsky Performance Scale score higher than 60, and an expected survival of more than 6 months. Two hundred and twenty-three patients (72%) consented to participate and completed the baseline questionnaire, and 71% completed the 3-month follow-up measures. Exposures: An a priori conceptual model including sociodemographics, medical variables, psychosocial risk factors, and a polygenic risk score for depression (PRS-D) was tested. Main outcomes and measures: The Functional Assessment of Cancer Therapy-Head and Neck measured QoL at baseline and at 3 months. Results: Participants were mostly men (68.7%), with a mean (range) age of 62.9 (31-92) years, 36.6% having a university degree, 35.6% living alone, and 71.4% diagnosed with advanced HNC with mostly cancers being of the oropharynx (42.2%), oral cavity (17%), and larynx (16.3%). QoL at 3 months after HNC diagnosis was associated with higher PRS-D (B = -4.71; 95% CI, -9.18 to -0.23), and a diagnosis of major depressive disorder within 2 weeks of an HNC diagnosis (B = -32.24; 95% CI, -51.47 to 13.02), lifetime suicidal ideation (B = -22.39; 95% CI, -36.14 to -8.65), living with someone (B = 12.48; 95% CI, 3.43-21.52), having smoked cigarettes in the past 30 days pre-HNC diagnosis (B = -15.50; 95% CI, -26.07 to -4.93), chemotherapy type (B = -11.13; 95% CI, -21.23 to -1.02), and total radiotherapy dose (Gy) (B = -0.008; 95% CI, -0.01 to -0.002). Conclusions and relevance: This study identified the predictive value of a genetic predisposition to depression on QoL and function immediately after oncologic treatments. These findings highlight the potential importance of genetic profiling pretreatment to identify those most susceptible to experience QoL and functional compromise. Depression is a clear area of public health concern and should be a central focus in the treatment of patients with HNC.
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 | 0.000 |
| Bibliometrics | 0.000 | 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".