Immunological biomarkers associated with brain structure and executive function in late‐life depression: exploratory pilot study
Bibliographic record
Abstract
OBJECTIVE: Several immunological biomarkers are altered in late-life major depressive disorder (LLD). Immunological alterations could contribute to LLD's consequences, but little is known about the relations between specific immunological biomarkers and brain health in LLD. We performed an exploratory pilot study to identify, from several candidates, the specific immunological biomarkers related to important aspects of brain health that are altered in LLD (brain structure and executive function). METHODS: Adults (n = 31) were at least 60 years old and had major depressive disorder. A multiplex immunoassay assessed 13 immunological biomarkers, and we examined their associations with structural MRI (grey matter volume and white matter hyperintensity volume (WMH)) and executive function (Color-Word Interference and Trail-Making tests) measures. RESULTS: Vascular endothelial growth factor (VEGF) and the chemokine eotaxin had significant negative associations with grey matter volume (VEGF: n = 31, r = -0.65; eotaxin: n = 29, r = -0.44). Tumor necrosis factor alpha (TNF-α) had a significant positive relationship with WMHs (n = 30, r = 0.52); interferon-γ (IFN-γ) and macrophage inflammatory protein-1α (MIP-1α) were also significantly associated with WMHs (IFN-γ: n = 31, r = 0.48; MIP-1α: n = 29, r = 0.45). Only eotaxin was associated with executive function (set-shifting performance as measured with the Trail-making test: n = 33, r = -0.43). CONCLUSIONS: Immunological markers are associated with brain structure in LLD. We found the immunological correlates of grey and white matter differ. Prospective studies are needed to evaluate whether these immunological correlates of brain health increase the risk of LLD's consequences. Eotaxin, which correlated with both grey matter volume and set-shifting performance, may be particularly relevant to neurodegeneration and cognition in LLD. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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".