An integrative approach to detecting potential blood-based biomarkers of cognitive frailty
Bibliographic record
Abstract
OBJECTIVE: Cognitive frailty, defined by the coexistence of cognitive decline and physical frailty, has been clinically defined, but its biological clues are still vague. This underscores the need for promising blood-based molecular biomarkers. DESIGN: Cross-sectional observational study. SETTINGS AND PARTICIPANTS: Frailty was diagnosed using the Japanese version of the Cardiovascular Health Study (J-CHS), and mild cognitive impairment was assessed with the Japanese version of the Montreal Cognitive Assessment (MoCA-J) and Mini-Mental State Examination-Japanese (MMSE-J). Participants with MMSE-J ≥24, MoCA-J score ≤25, and J-CHS score ≥1 were classified as having cognitive frailty. This study included 87 older adults aged ≥65 years, comprising 44 robust and 43 with cognitive frailty. MEASUREMENTS: Blood samples and associated clinical data were obtained from the National Center for Geriatrics and Gerontology Biobank in Japan. A multi-omics analysis integrating clinical data, RNA-seq, aging-related factors, and metabolomics were conducted to identify potential biomarkers through logistic regression, adjusting for age, sex, and body mass index (BMI). An optimal set of biomarkers was determined by constructing prediction models using the random forest algorithm. RESULTS: Three candidate biomarkers were identified from aging-related factors-growth differentiation factor (GDF15), brain-derived neurotrophic factor (BDNF), and Adiponectin-and three from metabolomics-myristic acid, nicotinamide, and γ-butyrobetaine. Using combinations of these candidates with clinical variables, we constructed risk prediction models. The best model incorporated one aging-related factors (GDF15) and two metabolites (myristic acid, and nicotinamide), achieving a high area under the receiver operating characteristic curve (AUC) of 0.96 in an independent validation cohort. This was significantly higher than models based solely on clinical information (age, sex, and BMI) (Welch's t-test, p <0.001). Among these biomarkers, myristic acid showed the highest influence, with a median Gini importance of 0.38 (95% confidence interval: 0.29-0.47). CONCLUSIONS: We identified three promising biomarkers-GDF15, myristic acid, and nicotinamide-for cognitive frailty. Notably, low plasma myristic acid levels emerged as the most significant contributor to the prediction model. Further refinement and large-scale validation will be essential to support its future clinical application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".