PLASMA NEUROFILAMENT LIGHT CHAIN IS ASSOCIATED WITH PHYSICAL FRAILTY IN OLDER ADULTS WITHOUT DEMENTIA
Bibliographic record
Abstract
Abstract Evidence suggests a link between brain biomarkers of neurodegeneration and physical function and frailty. However, the association of neurodegenerative biomarkers in the plasma with physical frailty in older adults remains unelucidated, as current evidence is limited and inconsistent. We tested the association between plasma neurofilament light chain (NFL), a plasma neurodegeneration biomarker, and physical frailty in a sample of community-dwelling older adults. We used baseline demographics, frailty, and plasma NFL data collected from 97 participants in a randomized controlled trial in community-dwelling, sedentary older adults without dementia [Montreal Cognitive Assessment (MoCA)>17]. Physical frailty was assessed using a composite measure of weakness, slow walking speed, unintentional weight loss, exhaustion, and low physical activity. Participants’ frailty levels were categorized into robustness, pre-intermediate frailty, and frailty. Multiple linear regression and ordinal logistic regression models adjusting for age, sex, race, education, and comorbidities were conducted to test the associations of NFL with frailty and levels of frailty. The sample was 70.0 ± 6.0 years old, with 80% being females, 72% having intact cognition, and 28% having mild cognitive impairment (MCI). In the adjusted models, higher plasma NFL was associated with higher frailty scores (β=0.16, 95%CI [Confidence Interval] = [0.06,0.26]) and a higher likelihood of being in a higher level of frailty group (odds ratio=1.28, 95% CI =1.02, 1.61). In summary, plasma NFL was associated with greater frailty in our sample of non-demented older adults. Plasma NFL may be a sensitive and promising neurodegenerative biomarker for physical frailty in older adults without dementia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".