Brain Iron in Aging Signature Regions Relating to Long‐Term Cognitive Decline in Older Adults
Bibliographic record
Abstract
Abstract Background Brain iron accumulation has been linked to cognitive decline in Alzheimer’s disease (AD), which is often associated with cerebral atrophy in AD‐specific brain regions. However, it remains unclear whether there is a selective pattern of brain iron deposition associated with cognitive aging or age‐related brain atrophy compared to AD. Therefore, this study aimed to investigate the relationship between brain iron level, atrophy, and cognitive decline in a Chinese population‐based cohort and explore the spatial distribution of brain iron deposition associated with individual cognition functions. Method A total 770 community‐dwelling participants from Taizhou Imaging Study (mean age 62.0 ± 4.93 years, 57.5% women) underwent brain MRI examination, and among them 219 participants accepted neuropsychological tests at a mean follow‐up of 2.68 years. Brain iron deposition was evaluated using quantitative susceptibility mapping (QSM), while global atrophy, cortical thickness and subcortical volumes were analyzed using surface‐based techniques. Global cognition function was assessed using the Mini‐Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), and domain‐specific cognitive scores were obtained from subscores of MoCA. Regional analyses were performed on the cortical and 3 signature regions: Aging‐specific, Aging and AD signature meta‐ROIs (Figure 1). Result Iron levels were negatively correlated with classic MRI markers of cortical atrophy (cortical thickness, gray matter volume, local gyrification index) in total, Aging‐specific, and Aging signature cortical regions (all P < 0.05). Participants in the upper tertile of the cortical and Aging‐specific signature QSM showed worse global cognitive function compared to those in the bottom tertile after adjusting for gray matter atrophy. Among 219 participants who underwent follow‐up, higher brain iron levels in all ROIs predicted accelerated deterioration in the rate of cognitive decline in global cognition, attention, and visuospatial function (Table 1 and Table 2, all P < 0.05). These associations remained significant even after the including cortical thickness of each ROI as a covariate. Conclusion The present study demonstrates that aging and AD‐selective iron deposition is associated with atrophy and cognitive decline in elderly individuals, suggesting the brain iron deposition has the potential to be used as an imaging biomarker for tracking cognitive aging.
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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 | 0.001 |
| 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".