Examining Potential Biomarkers for Depression Diagnosis: A Literature Review
Bibliographic record
Abstract
Introduction: Biomarkers in depression show potential in providing insight into the pathophysiology of the disorder and subsequent treatment plans. Within research, there have been many prospective biomarkers such as endocrine markers, epigenetics, inflammatory markers, cytokines, neuroimaging, growth factors, and more. Based on recent studies, we propose three promising biomarkers associated with diagnosing major depressive disorder (MDD): growth factors, endocrine markers, and neuroimaging. Methods: Literature searches were performed using databases PsychINFO, PubMed, and Scopus, and a total of seventeen articles were used. Results: Physical changes in brain volume and thickness of specific brain regions have been associated with the occurrence of MDD such as reduced hippocampal volume in depressed patients along with thinning of the right para-hippocampus. Additionally, progressive cortical thickening in the left inferior central and pre-frontal gyrus has been observed in patients developing MDD. Brain derived neurotrophic factor (BDNF), a growth factor, could be a potential biomarker for diagnosing MDD as BDNF plays an important role in neuronal development, neuronal survival, and regulating neurotransmitter systems. Depressed individuals exhibit decreased BDNF levels, specifically in the hippocampus and prefrontal lobes. Three hormones that have been of primary interest related to MDD biomarkers include cortisol, thyroid stimulating hormone (TSH), and prolactin. These hormones are involved in the diathesis-stress response mediated by the activation of the hypothalamus-pituitary-adrenal (HPA) axis. Elevated levels of these hormones were observed in depressive patients. Discussion: Following an in-depth analysis of neuroimaging, BDNF, and hormones, differences between MDD patients and control groups were observed. Cortical thickness, functional connectivity, and brain activity (blood flow) alterations were all reported in neuroimaging studies. Mainly, decreases in BDNF levels and alterations of hormones were all observed. Each biomarker requires further investigation and limitations that must be considered. Conclusion: Overall, the literature review on prospective MDD biomarkers suggests abnormalities in BDNF, cortisol, TSH, and prolactin levels in MDD patients. Structural brain differences were also observed through neuroimaging. Ultimately, studying biomarkers would allow us to better visualize how depression affects the body, allowing for the development of diverse diagnostic and treatment courses.
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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 | 0.020 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.006 |
| 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".