The relationship between health-related quality of life and melancholic depressive symptoms is modified by brain insulin receptor gene network
Bibliographic record
Abstract
To investigate whether expression-based polygenic risk scores for the insulin receptor gene network (ePRS-IRs) modifiy the association between type of depressive symptoms and health-related quality of life (HRQoL). This cross-sectional study includes 1558 individuals from the Helsinki Birth Cohort Study. Between 2001 and 2004, the Short Form-36 questionnaire was employed to assess mental and physical components of HRQoL and Beck Depression Inventory (BDI) to assess depressive symptoms. Depressive symptoms were categorized into minimal (BDI < 10), non-melancholic and melancholic types of depression. The ePRS-IRs were calculated for the hippocampal (hePRS-IR) and the mesocorticolimbic (mePRS-IR) regions of the brain. General linear regression models adjusted for age, sex, population stratification, lifestyle factors and body mass index were applied to analyze the data. Both types of depressive symptoms were associated with lower HRQoL (p < 0.0001). HePRS-IR modified the association between the types of depression and mental HRQoL (p for interaction = 0.005). Melancholic type of depressive symptoms was associated with higher mental HRQoL compared to the non-melancholic symptoms among individuals with low hePRS-IR (adjusted mean 4.1, 95% CI 0.7-7.4, p = 0.018). However, no such difference was evident in moderate or high hePRS-IR groups as higher hePRS-IR was associated with lower mental HRQoL (B = - 3.4, 95% CI - 5.6 to - 1.2) in individuals with melancholic type of depressive symptoms. No direct associations were detected between the ePRS-IRs and type of depressive symptoms or HRQoL. Variations in the glucose-insulin metabolism can lower HRQoL in individuals with melancholic depressive symptoms.
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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".