Cerebrospinal fluid biomarkers in older adults with mild cognitive impairment, with and without a major depressive disorder
Bibliographic record
Abstract
Abstract Background Mild Cognitive Impairment (MCI) and Major Depressive Disorder (MDD) have been independently associated with increased risk of dementia. Cerebrospinal fluid (CSF) biomarkers associated with Alzheimer’s disease (AD) show changes prior to the onset of symptoms of dementia or neuroimaging biomarkers of AD. We examined the association between a CSF biomarker profile of AD and diagnosis in three groups: MCI alone; a diagnosis of MDD alone; or MCI plus a diagnosis of MDD. Method The CSF total tau, p‐tau, amyloid‐β42, and the p‐tau/ amyloid‐β42 ratio were measured in 31 participants enrolled in the PACt‐MD study diagnosed with MCI (N=13), MDD (N=7), or both (MCI+MDD) (N=11) according to NIA‐AA and DSM IV criteria. We compared AD biomarkers in the 3 groups and then compared cognitive performance in those with and without a CSF biomarker profile consistent with AD Result Among the 31 participants, 9 had a CSF biomarker profile consistent with AD: 7/13 with MCI; 0/7 with MDD; and 2/11 with MCI+MDD. Participants with an AD biomarker profile had significantly greater impairment in verbal memory than those without one (p=0.009). Of those without a profile consistent with AD, no significant differences were observed in cognitive performance between the MCI and MDD+MCI groups. Conclusion In our sample, few participants with MDD had a CSF biomarker profile consistent with AD, even if they had a neurocognitive profile consistent with MCI. The etiopathology of cognitive impairment in older patients with MDD requires further investigation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".