Identification of circulating risk biomarkers for cognitive decline in a large community‐based population in Chongqing China
Bibliographic record
Abstract
INTRODUCTION: This study aims to investigate the relationship between blood-based pathologies and established risk factors for cognitive decline in the community-based population of Chongqing, a region with significant aging. METHODS: A total of 26,554 residents aged 50 years and older were recruited. Multinomial logistic regression models were applied to assess the risk factors of cognition levels. Propensity score matching and linear mixed effects models were used to evaluate the relationship between key risk factors and the circulating biomarkers. RESULTS: Shared and distinct risk factors for MCI and dementia were identified. Age, lower education, medical history of stroke, hypertension, and epilepsy influenced mild cognitive impairment (MCI) progression to dementia. Correlations between key risk factors and circulating neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), amyloid β protein (Aβ)40, and Aβ42/Aβ40 ratio suggest underlying mechanisms contributing to cognitive impairment. DISCUSSION: The common and distinct risk factors across cognitive decline stages emphasize the need for tailored interventions. The correlations with blood biomarkers provide insights into potential management targets. HIGHLIGHTS: From a large community-based cohort study on the residents in Chongqing, we have identified that mild cognitive impairment (MCI) and dementia share several common risk factors, including age, female gender, rural living, lower education levels, and a medical history of stroke. However, each condition also has its own unique risk factors. Several factors contribute to the progression of MCI into dementia including age, education levels, occupation, and a medical history of hypertension and epilepsy. We discover the correlations between the risk factors for dementia and blood biomarkers that indicate the presence of axonal damage, glial activation, and Aβ pathology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".