Associations between peripheral inflammation and clinical phenotypes of bipolar depression in a lower-middle income country
Bibliographic record
Abstract
OBJECTIVE: There has been increased interest in repurposing anti-inflammatories for the treatment of bipolar depression. Evidence from high-income countries suggests that these agents may work best for specific depressive symptoms in a subset of patients with biochemical evidence of inflammation but data from lower-middle income countries (LMICs) is scarce. This secondary analysis explored the relationship between pretreatment inflammatory markers and specific depressive symptoms, clinical measures, and demographic variables in participants with bipolar depression in Pakistan. METHODS: The current study is a cross-sectional secondary analysis of a randomized controlled trial of two anti-inflammatory medications (minocycline and celecoxib) for bipolar depression (n = 266). A series of logistic and linear regression models were completed to assess the relationship between C-reactive protein (CRP) (CRP > or < 3 mg/L and log10CRP) and clinical and demographic features of interest and symptoms of depression. Baseline clinical trial data was used to extract clinical and demographic features and symptoms of depression were assessed using the 24-item Hamilton Depression Rating Scale. RESULTS: The prevalence of low-grade inflammation (CRP > 3 mg/L) in the sample was 70.9%. After adjusting for baseline body mass index, socioeconomic status, age, gender, symptoms related to anhedonia, fatigue, and motor retardation were most associated with low-grade inflammation. CONCLUSIONS: Bipolar disorder (BD) patients from LMICs may experience higher rates of peripheral inflammation than have been reported in Western populations with BD. Future trials of repurposed anti-inflammatory agents that enrich for participants with these symptom profiles may inform the development of personalized treatment for bipolar depression in LMICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".