Frailty is characterized by biomarker patterns reflecting inflammation or muscle catabolism in multi‐morbid patients
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Frailty development is partly dependent on multiple factors like low levels of nutrients and high levels of oxidative stress (OS) and inflammation potentially leading to a muscle-catabolic state. Measures of specific biomarker patterns including nutrients, OS and inflammatory biomarkers as well as muscle related biomarkers like 3-methylhistidine (3MH) may improve evaluation of mechanisms and the complex networks leading to frailty. METHODS: In 220 multi-morbid patients (≥ 60 years), classified as non-frail (n = 104) and frail (n = 116) according to Fried's frailty criteria, we measured serum concentrations of fat-soluble micronutrients, amino acids (AA), OS, interleukins (IL) 6 and 10, 3MH (biomarker for muscle protein turnover) and serum spectra of fatty acids (FA). We evaluated biomarker patterns by principal component analysis (PCA) and their cross-sectional associations with frailty by multivariate logistic regression analysis. RESULTS: together with high negative FL of pro-inflammatory FA, IL6 and IL6/IL10, reflecting an inflammation-related pattern. PC2 was characterized by high positive FL of AA together with high negative FL of 3MH-based biomarkers, reflecting a muscle-related pattern. Frail patients had significantly lower factor scores than non-frail patients for both PC1 [median: -0.27 (interquartile range: 1.15) vs. 0.27 (1.23); P = 0.001] and PC2 [median: -0.15 (interquartile range: 1.13) vs. 0.21 (1.38); P = 0.002]. Patients with higher PC1 or PC2 factor scores were less likely to be frail [odds ratio (OR): 0.62, 95% CI: 0.46-0.83, P = 0.001 for PC1; OR: 0.64, 95% CI: 0.48-0.86, P = 0.003 for PC2] compared with patients with lower PC1 or PC2 factor scores. This indicates that increasing levels of anti-inflammatory biomarkers and increasing levels of muscle-anabolic biomarkers are associated with a reduced likelihood (38% and 36%, respectively) for frailty. Significant associations remained after adjusting the regression models for potential confounders. CONCLUSIONS: We conclude that two specific patterns reflecting either inflammation-related or muscle-related biomarkers are both significantly associated with frailty among multi-morbid patients and that these specific biomarker patterns are more informative than single biomarker analyses considering frailty identification.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it