Depression with inflammation: longitudinal analysis of a proposed depressive subtype in community dwelling older adults
Bibliographic record
Abstract
OBJECTIVE: It has been proposed that inflammation may be causally related to depression. If this is the case, it may be possible to distinguish an inflammatory depressive subtype according to illness course, pattern of co-morbidity and symptom profile. METHODS: Eight hundred and eleven community dwelling older adults with depression (8 item Center for Epidemiologic Studies scale ≥ 4) from the English Longitudinal study of Ageing (ELSA) were followed for a median of 47 months. Participants with depression and inflammation (C Reactive Protein > 3 mg/l) were compared to those with depression alone. RESULTS: In a longitudinal analysis, depression with associated inflammation was more likely to persist over time. This association was independent of baseline depression severity and medical co-morbidity (OR 1.47 95% CI 1.03 - 2.10, p = 0.034) but was no longer significant following further adjustment for body mass index (OR 1.37 95% CI 0.94 - 2.01, p = 0.106). Inflammation either partially or completely mediated the association between medical co-morbidity, body mass index and depression at follow-up. Depression with inflammation was associated with more amotivation, less sadness, greater medical co-morbidity and higher body mass index. CONCLUSIONS: Our findings provide some support for an inflammatory contribution to depression. This subgroup has a worse prognosis and may benefit from interventions targeting co-morbidity, body mass index and associated inflammation. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".