Baseline CSF ferritin levels were associated with trajectories of depressive symptoms among older people without dementia
Bibliographic record
Abstract
Background Previous studies have suggested a link between ferritin levels in the blood and depressive symptoms. However, no prior studies have investigated the association between cerebrospinal fluid (CSF) total ferritin, ferritin light chain, and ferritin heavy chain and longitudinal changes in depressive symptoms among older people without dementia. Methods In this study, 543 older people without dementia were included, comprising 163 cognitively unimpaired (CU) participants and 380 participants with mild cognitive impairment (MCI). A non-parametric k-means longitudinal cluster analysis was performed to identify distinct trajectories of depressive symptoms, which were measured using the 15-item Geriatric Depression Scale (GDS-15) over a period of 5 years. Multinomial logistic regression models were used to examine the relationship between CSF total ferritin, ferritin light chain, and ferritin heavy chain levels and the trajectories of depressive symptoms, adjusting for potential covariates. Results We identified three distinct trajectories of depressive symptoms: consistently low (trajectory 1, n = 364; mean age: 73 ± 7 years; percentage of females: 43%), moderately increasing (trajectory 2, n = 149; mean age: 72 ± 7 years; percentage of females: 43%), and rapidly increasing (trajectory 3, n = 30; mean age: 72 ± 8 years; percentage of females: 47%). Compared with trajectory 1, there was a significant relationship between membership in trajectory 3 and CSF total ferritin levels (OR = 0.04, 95% CI = 0.01 to 0.18, p < 0.001). Similarly, CSF ferritin light chain and heavy chain levels showed a similar pattern to that of CSF ferritin levels. Conclusion Our study identified three distinct trajectories of depressive symptoms in older adults without dementia. We observed that lower CSF ferritin levels were associated with a higher likelihood of membership in the rapidly increasing symptom trajectory.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".