Frailty is characterized by biomarker patterns reflecting inflammation or muscle catabolism in multi-morbid patients.

Abstract

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. In 220 multi-morbid patients (≥ 60years), 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. Two biomarker patterns [principal components (PC)] were identified by PCA. PC1 was characterized by high positive factor loadings (FL) of carotenoids, anti-inflammatory FA and vitamin D3 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. 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.