Immune and metabolic disturbance as a function of genetic risk and phase of illness in major depression
Bibliographic record
Abstract
Immune and metabolic factors are important in the pathophysiology of major depressive disorder (MDD) but we know little about how these factors manifest in relation to the status of depressive illness-from genetic risk for MDD, to a depressive episode, to depression in remission. Using genetic, diagnostic, biometric, and blood-bioassay data from the UK Biobank, we examined measures of pro-inflammatory signaling (C-reactive protein) and metabolic dysfunction (metabolic syndrome symptomatology) in females (N = 37,806) and males (N = 17,946) as a function of polygenic load for MDD (high versus low) interacting with depression status (never depressed, currently depressed, or depression in remission). We examined socioeconomic status (SES) as an exploratory factor in this design. Groups were matched for several confounders using a propensity-matching algorithm (females: n = 6301 per group for N = 37,806; n = 2991 per group for N = 17,946). In females we found increased inflammation and metabolic dysfunction in the higher-versus-lower PRS quartile, in those below-versus-above the median SES, and in those suffering currently from depression relative to their remitted depressed and healthy counterparts. This association remained when considering only non-psychotropic-medicated persons. Nonetheless, we also saw in both male and female samples that measures of immunological and metabolic dysfunction increased with increasing anti-depressant medication load. We discuss these findings in terms of the epidemiological significance of immune and metabolic functioning in depression and their paradoxical relation with antidepressant treatment.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Biobank study of inflammation and metabolic dysfunction in major depression.
The study examines biological and clinical features of depression rather than research itself.
Clinical epidemiology of inflammation and metabolism in depression, not research methods.
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".