Exploring the clinical characteristics and etiological factors of comorbid major depressive disorder and social anxiety disorder
Bibliographic record
Abstract
The comorbidity between the major depressive disorder (MDD) and the social anxiety disorder (SAD) is significantly prevalent, necessitating a nuanced understanding of their overlapping clinical characteristics and shared etiological factors, including inflammatory biomarkers. To address this, we conducted a cross-sectional study from December 2021 to June 2022, encompassing 204 outpatients diagnosed with MDD-SAD comorbidity. We employed various psychometric assessments, such as the Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Childhood Trauma Questionnaire (CTQ-28), Toronto Alexithymia Scale (TAS-20) and the Liebowitz Social Anxiety Scale (LSAS). Additionally, we analyzed inflammatory biomarkers including the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte platelet ratio (NLPR), systemic inflammation index (SII), and the systemic inflammation response index (SIRI). Our findings accentuated that patients primarily diagnosed with MDD exhibited elevated levels of certain inflammatory biomarkers. They reported more severe and atypical depressive symptoms (75.7% vs 58.5%; P = 0.010) and had significantly higher CTQ-28 subscale scores (P < 0.05). Our study unveils a complex relationship between MDD and SAD, with significant disparities in the symptom severity and inflammatory biomarker levels, thereby establishing a compelling case for dual-diagnosis treatment approaches. It elucidates the critical role of inflammation in the comorbidity of MDD and SAD, marking a pioneering step towards more comprehensive and holistic patient care strategies. These insights could potentially revolutionize therapeutic approaches in psychiatric care, promising significantly improved outcomes through early detection and integrated intervention strategies.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".