Association Between Mean Corpuscular Volume and Cognitive Function in Community‐Dwelling Older Adults ‐ An 11‐year Cohort Study
Bibliographic record
Abstract
Abstract Background The mean corpuscular volume (MCV) is a common parameter in routine blood tests. Larger MCV tends to be more fragile and pose challenges in passing through capillaries, leading to a diminished capacity for oxygen and nutrient supply to the brain thus impacting cognitive function. Limited studies have explored the association between MCV and cognitive impairment with inconsistent results. This study aimed to investigate the association between MCV and cognitive function in community‐dwelling older adults. Method This 11‐year cohort study (2011‐2022) included 530 non‐demented older adults (aged above 65) from the ongoing Taiwan Initiative for Geriatric Epidemiological Research at baseline (2011 to 2013) with four biennial follow‐ups. Global cognition was assessed by the Taiwanese version of the Montreal Cognitive Assessment. Domain‐specific cognition, including memory, attention, verbal fluency, and executive function, was assessed by a battery of neuropsychiatric tests. Blood data were collected, including MCV, red blood cell distribution width, hemoglobin, platelet, and vitamin B12. The cutoff of MCV was determined by the turning point obtained from the generalized additive model. This study utilized a generalized linear mixed model to analyze the association between MCV and cognitive function adjusted for age, sex, years of education, apolipoprotein E ε4 status, years of follow‐up, and other blood parameters. Result The average age of the participants is 72.68 years, with females comprising 53.40%. As the follow‐up time increased, older adults with larger baseline MCV (>93.0 fL) exhibited a significantly poor performance of memory (immediate free recall: = ‐0.028, p = 0.021; delayed free recall: = ‐0.039, p = 0.002; delayed theme recall: = ‐0.041, p = 0.003), and attention (digit span forward: = ‐0.028, p = 0.027). Additionally, an interaction was found between MCV and age on global cognition (p interaction=0.008). Conclusion Over time, increased MCV was associated with poor domain‐specific cognition. MCV may potentially serve as an early indicator for assessing cognitive impairment. Larger and longer‐term studies are needed to clarify their temporal relationship and underlying mechanism.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".