Associations between C-reactive protein and individual symptoms of depression in a lower-middle income country
Bibliographic record
Abstract
BACKGROUND: Data on associations between inflammation and depressive symptoms largely originate from high income population settings, despite the greatest disease burden in major depressive disorder being attributed to populations in lower-middle income countries (LMICs). AIMS: We assessed the prevalence of low-grade inflammation in adults with treatment-resistant depression (TRD) in Pakistan, an LMIC, and investigated associations between peripheral C-reactive protein (CRP) levels and depressive symptoms. METHOD: = 191). Logistic regression models were built to assess the relationship between pre-treatment CRP (≥ or <3 mg/L) and individual depressive symptoms measured using the Hamilton Depression Rating Scale. Descriptive statistics and regression were used to assess treatment response for inflammation-associated symptoms. RESULTS: = 146) of participants. Early night insomnia (odds ratio 2.33, 95% CI 1.16-5.25), early morning waking (odds ratio 2.65, 95% CI 1.29-6.38) and psychic anxiety (odds ratio 3.79, 95% CI 1.39-21.7) were positively associated, while gastrointestinal (odds ratio 0.38, 95% CI 0.14-0.86) and general somatic symptoms (odds ratio 0.34, 95% CI 0.14-0.74) were negatively associated with inflammation. Minocycline, but not simvastatin, improved symptoms positively associated with inflammation. CONCLUSIONS: The prevalence of inflammation in this LMIC sample with TRD was higher than that reported in high income countries. Insomnia and anxiety symptoms may represent possible targets for personalised treatment with immunomodulatory agents in people with elevated CRP. These findings require replication in independent clinical samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".