Inflammatory biomarkers as predictors for unlocking antidepressant efficacy: Assessing predictive value and risk stratification in major depressive disorder in a prospective longitudinal study
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder (MDD) is characterized by significant heterogeneity in treatment response, with inflammation hypothesized to play a role in its pathophysiology. Peripheral inflammatory biomarkers, such as the neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP), may predict antidepressant efficacy. This study investigated the association between baseline inflammatory biomarkers, their changes, and antidepressant treatment outcomes in patients with MDD. METHODS: A prospective longitudinal cohort study in Qatar recruited 123 MDD outpatients (aged 18-64). Baseline assessments included NLR, CRP, monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR). Depression severity was measured via the Zung Self-Rating Depression Scale (ZSRS) at baseline and 12 weeks post-treatment. Statistical analyses, including multiple regression and Random Forest machine learning models, identified predictors of antidepressant response. RESULTS: Improvement in depressive symptoms was associated with female sex, higher mean corpuscular volume (MCV), lower absolute neutrophil count (ANC), and higher eosinophil counts. However, changes in NLR, MLR, PLR, and CRP did not predict treatment response. Folate levels and PLR were identified by the machine learning model as top predictors, suggesting potential utility as biomarkers for response classification. Our study identified predictors of improvement in suicidal ideation, including hematological markers (lower RBC, higher eosinophils, lower monocytes), younger age, female sex, medical comorbidities, and longer assessment intervals. CONCLUSION: Baseline ANC and eosinophil count may help stratify MDD treatment outcomes, though post-treatment biomarker changes were not linked to symptom improvement. Our findings highlight suicidality as a distinct pathology within depression, necessitating tailored interventions. This study highlights the complexity of inflammation in depression and suicidality, emphasizing the need for advanced biomarkers utilization in precision medicine and personalized psychiatry treatment.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".