Impacts of Kidney Dysfunction and Cerebral Cortical Thinning on Cognitive Change in Elderly Population
Bibliographic record
Abstract
BACKGROUND: Cerebral cortical thickness is a neuroimaging biomarker to predict cognitive decline, and kidney dysfunction (KD) is associated with cortical thinning. OBJECTIVE: This study aimed to investigate the effects of KD and cortical thinning on cognitive change in a prospective cohort study. METHODS: A total of 244 non-demented participants were recruited from elderly health checkup program and received cognitive exams including Montreal Cognitive Assessment (MoCA) and different cognitive domains at baseline and three biannual follow-ups afterwards. KD was defined as having either glomerular filtration rate <60 ml/min/1.73 m2 or proteinuria. Cortical thickness of global, lobar, and Alzheimer's disease (AD) signature area were derived from magnetic resonance imaging at baseline, and cortical thinning was defined as the lowest tertile of cortical thickness. Generalized linear mixed models were applied to evaluate the effects of KD and cortical thinning on cognitive changes. RESULTS: KD was significantly associated with the decline in attention function (β= -0.29). Thinning of global (β= -0.06), AD signature area (β= -0.06), temporal (β= -0.06), and parietal lobes (β= -0.06) predicted poor verbal fluency over time, while temporal lobe thinning also predicted poor MoCA score (β= -0.19). KD modified the relationship between thinning of global, frontal, and limbic, and change of logical memory function (pinteraction < 0.05). When considering jointly, participants with both KD and cortical thinning had greatest decline in attention function compared with those without KD or cortical thinning (β= -0.51, ptrend = 0.008). CONCLUSIONS: KD and cortical thinning have joint effect on cognitive decline, especially the attention function. Reverse associations may exist between cortical thinning and memory function in participants with KD, though the results should be interpreted cautiously as an exploratory analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".